Uses of Interface
de.lmu.ifi.dbs.elki.database.relation.Relation

Packages that use Relation

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.benchmark	Benchmarking pseudo algorithms.
de.lmu.ifi.dbs.elki.algorithm.classification	Classification algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering	Biclustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash	Helper classes for the CASH algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch	BIRCH clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional	Clustering algorithms for one-dimensional data.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings These methods are mostly useful for providing a reference result in evaluation.
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased	Angle-based outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel	Parallel implementations of distance-based outlier detectors.
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic	Outlier detection algorithms based on intrinsic dimensionality.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel	Parallelized variants of LOF.
de.lmu.ifi.dbs.elki.algorithm.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood	Spatial outlier neighborhood classes
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted	Weighted Neighborhood definitions
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace	Subspace outlier detection methods Methods that detect outliers in subspaces (projections) of the data set.
de.lmu.ifi.dbs.elki.algorithm.outlier.svm	Support-Vector-Machines for outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
de.lmu.ifi.dbs.elki.algorithm.projection	Data projections (see also preprocessing filters for basic projections).
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms.
de.lmu.ifi.dbs.elki.algorithm.timeseries	Algorithms for change point detection in time series.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.query.distance	Prepared queries for distances
de.lmu.ifi.dbs.elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries
de.lmu.ifi.dbs.elki.database.query.range	Prepared queries for ε-range queries, that return all objects within the radius ε
de.lmu.ifi.dbs.elki.database.query.rknn	Prepared queries for reverse k nearest neighbor (rkNN) queries
de.lmu.ifi.dbs.elki.database.query.similarity	Prepared queries for similarity functions
de.lmu.ifi.dbs.elki.database.relation	Relations, materialized and virtual (views)
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter	Distance functions deriving distances from, e.g., similarity measures
de.lmu.ifi.dbs.elki.distance.distancefunction.external	Distance functions using external data sources
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
de.lmu.ifi.dbs.elki.distance.distancefunction.set	Distance functions for binary and set type data.
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace	Distance functions based on subspaces
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster	Similarity measures for comparing clusters.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
de.lmu.ifi.dbs.elki.evaluation.similaritymatrix	Render a distance matrix to visualize a clustering-distance-combination.
de.lmu.ifi.dbs.elki.index	Index structure implementations
de.lmu.ifi.dbs.elki.index.distancematrix	Precomputed distance matrix.
de.lmu.ifi.dbs.elki.index.idistance	iDistance is a distance based indexing technique, using a reference points embedding.
de.lmu.ifi.dbs.elki.index.invertedlist	Indexes using inverted lists.
de.lmu.ifi.dbs.elki.index.lsh	Locality Sensitive Hashing
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH
de.lmu.ifi.dbs.elki.index.preprocessed	Index structure based on preprocessors
de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics	Preprocessed index used by the FastOPTICS algorithm.
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.preprocessed.localpca	Index using a preprocessed local PCA
de.lmu.ifi.dbs.elki.index.preprocessed.preference	Indexes storing preference vectors
de.lmu.ifi.dbs.elki.index.preprocessed.snn	Indexes providing nearest neighbor sets
de.lmu.ifi.dbs.elki.index.projected	Projected indexes for data
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree	Cover-tree variations.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees	Metrical index structures based on the concepts of the M-Tree supporting processing of reverse k nearest neighbor queries by using the k-nn distances of the entries
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp	MkAppTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop	MkCoPTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax	MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab	MkTabTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	MTree
de.lmu.ifi.dbs.elki.index.tree.spatial.kd	K-d-tree and variants
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat	FlatRStarTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	RdKNNTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.index.vafile	Vector Approximation File
de.lmu.ifi.dbs.elki.math.linearalgebra	The linear algebra package provides classes and computational methods for operations on matrices and vectors.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing
de.lmu.ifi.dbs.elki.math.scales	Scales handling for plotting
de.lmu.ifi.dbs.elki.math.spacefillingcurves	Space filling curves
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.result.textwriter	Text serialization (CSV, Gnuplot, Console, ...)
de.lmu.ifi.dbs.elki.utilities.referencepoints	Package containing strategies to obtain reference points Shared code for various algorithms that use reference points
de.lmu.ifi.dbs.elki.visualization	Visualization package of ELKI
de.lmu.ifi.dbs.elki.visualization.parallel3d	3DPC: 3D parallel coordinate plot visualization for ELKI.
de.lmu.ifi.dbs.elki.visualization.parallel3d.layout	Layouting algorithms for 3D parallel coordinate plots.
de.lmu.ifi.dbs.elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations
de.lmu.ifi.dbs.elki.visualization.visualizers.histogram	Visualizers based on 1D projected histograms
de.lmu.ifi.dbs.elki.visualization.visualizers.parallel	Visualizers based on parallel coordinates
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot	Visualizers based on scatterplots
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.index	Visualizers for index structures based on 2D projections
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier	Visualizers for outlier scores based on 2D projections
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.uncertain	Visualizers for uncertain data.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation
tutorial.outlier	Tutorials on implementing outlier detection methods in ELKI.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm

Methods in de.lmu.ifi.dbs.elki.algorithm that return Relation

Modifier and Type	Method and Description
Relation<KNNList>	KNNJoin.run(Relation<V> relation) Joins in the given spatial database to each object its k-nearest neighbors.

Methods in de.lmu.ifi.dbs.elki.algorithm with parameters of type Relation

Modifier and Type	Method and Description
CorrelationAnalysisSolution<V>	DependencyDerivator.generateModel(Relation<V> db, DBIDs ids) Runs the pca on the given set of IDs.
CorrelationAnalysisSolution<V>	DependencyDerivator.generateModel(Relation<V> relation, DBIDs ids, double[] centroid) Runs the pca on the given set of IDs and for the given centroid.
Result	DummyAlgorithm.run(Database database, Relation<O> relation) Run the algorithm.
KNNDistancesSampler.KNNDistanceOrderResult	KNNDistancesSampler.run(Database database, Relation<O> relation) Provides an order of the kNN-distances for all objects within the specified database.
CorrelationAnalysisSolution<V>	DependencyDerivator.run(Database database, Relation<V> relation) Computes quantitatively linear dependencies among the attributes of the given database based on a linear correlation PCA.
Relation<KNNList>	KNNJoin.run(Relation<V> relation) Joins in the given spatial database to each object its k-nearest neighbors.
WritableDataStore<KNNList>	KNNJoin.run(Relation<V> relation, DBIDs ids) Inner run method.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.benchmark

Methods in de.lmu.ifi.dbs.elki.algorithm.benchmark with parameters of type Relation

Modifier and Type	Method and Description
Result	RangeQueryBenchmarkAlgorithm.run(Database database, Relation<O> relation) Run the algorithm, with a separate query set.
Result	KNNBenchmarkAlgorithm.run(Database database, Relation<O> relation) Run the algorithm.
Result	ValidateApproximativeKNNIndex.run(Database database, Relation<O> relation) Run the algorithm.
Result	RangeQueryBenchmarkAlgorithm.run(Database database, Relation<O> relation, Relation<NumberVector> radrel) Run the algorithm, with separate radius relation
Result	RangeQueryBenchmarkAlgorithm.run(Database database, Relation<O> relation, Relation<NumberVector> radrel) Run the algorithm, with separate radius relation

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.classification

Fields in de.lmu.ifi.dbs.elki.algorithm.classification declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends ClassLabel>	KNNClassifier.labelrep Class label representation.

Methods in de.lmu.ifi.dbs.elki.algorithm.classification with parameters of type Relation

Modifier and Type	Method and Description
void	Classifier.buildClassifier(Database database, Relation<? extends ClassLabel> classLabels) Performs the training.
void	PriorProbabilityClassifier.buildClassifier(Database database, Relation<? extends ClassLabel> labelrep) Learns the prior probability for all classes.
void	KNNClassifier.buildClassifier(Database database, Relation<? extends ClassLabel> labels)

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering with parameters of type Relation

Modifier and Type	Method and Description
protected void	GriDBSCAN.Instance.buildGrid(Relation<V> relation, int numcells, double[] offset) Build the data grid.
protected void	DBSCAN.expandCluster(Relation<O> relation, RangeQuery<O> rangeQuery, DBIDRef startObjectID, ArrayModifiableDBIDs seeds, FiniteProgress objprog, IndefiniteProgress clusprog) DBSCAN-function expandCluster.
Clustering<Model>	SNNClustering.run(Database database, Relation<O> relation) Perform SNN clustering
Clustering<PrototypeModel<O>>	CanopyPreClustering.run(Database database, Relation<O> relation) Run the algorithm
Clustering<MeanModel>	NaiveMeanShiftClustering.run(Database database, Relation<V> relation) Run the mean-shift clustering algorithm.
Clustering<PrototypeModel<O>>	Leader.run(Relation<O> relation) Run the leader clustering algorithm.
Clustering<Model>	DBSCAN.run(Relation<O> relation) Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.run(Relation<V> relation) Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.Instance.run(Relation<V> relation) Performs the DBSCAN algorithm on the given database.
protected void	DBSCAN.runDBSCAN(Relation<O> relation, RangeQuery<O> rangeQuery) Run the DBSCAN algorithm

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation with parameters of type Relation

Modifier and Type	Method and Description
double[][]	DistanceBasedInitializationWithMedian.getSimilarityMatrix(Database db, Relation<O> relation, ArrayDBIDs ids)
double[][]	AffinityPropagationInitialization.getSimilarityMatrix(Database db, Relation<O> relation, ArrayDBIDs ids) Compute the initial similarity matrix.
double[][]	SimilarityBasedInitializationWithMedian.getSimilarityMatrix(Database db, Relation<O> relation, ArrayDBIDs ids)
Clustering<MedoidModel>	AffinityPropagationClusteringAlgorithm.run(Database db, Relation<O> relation) Perform affinity propagation clustering.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering declared as Relation

Modifier and Type	Field and Description
protected Relation<V>	AbstractBiclustering.relation Relation we use.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering that return Relation

Modifier and Type	Method and Description
Relation<V>	AbstractBiclustering.getRelation() Getter for the relation.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering with parameters of type Relation

Modifier and Type	Method and Description
Clustering<M>	AbstractBiclustering.run(Relation<V> relation) Prepares the algorithm for running on a specific database.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as Relation

Modifier and Type	Field and Description
private Relation<ParameterizationFunction>	CASH.fulldatabase The entire relation.
private Relation<V>	HiCO.Instance.relation Data relation.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return Relation

Modifier and Type	Method and Description
private Relation<ParameterizationFunction>	CASH.preprocess(Relation<V> vrel) Preprocess the dataset, precomputing the parameterization functions.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with parameters of type Relation

Modifier and Type	Method and Description
private void	ORCLUS.assign(Relation<V> database, java.util.List<ORCLUS.ORCLUSCluster> clusters) Creates a partitioning of the database by assigning each object to its closest seed.
private MaterializedRelation<ParameterizationFunction>	CASH.buildDB(int dim, double[][] basis, DBIDs ids, Relation<ParameterizationFunction> relation) Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private Database	CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation, CASHInterval interval) Builds a database for the derivator consisting of the ids in the specified interval.
private Database	CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation, DBIDs ids) Builds a database for the derivator consisting of the ids in the specified interval.
private double[]	CASH.determineMinMaxDistance(Relation<ParameterizationFunction> relation, int dimensionality) Determines the minimum and maximum function value of all parameterization functions stored in the specified database.
private static int	CASH.dimensionality(Relation<ParameterizationFunction> relation) Get the dimensionality of a vector field.
private Clustering<Model>	CASH.doRun(Relation<ParameterizationFunction> relation, FiniteProgress progress) Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
private java.util.List<java.util.List<Cluster<CorrelationModel>>>	ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult, Relation<V> relation, int dimensionality, ERiCNeighborPredicate.Instance npred) Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.
private double[][]	ORCLUS.findBasis(Relation<V> database, ORCLUS.ORCLUSCluster cluster, int dim) Finds the basis of the subspace of dimensionality dim for the specified cluster.
private LMCLUS.Separation	LMCLUS.findSeparation(Relation<NumberVector> relation, DBIDs currentids, int dimension, java.util.Random r) This method samples a number of linear manifolds an tries to determine which the one with the best cluster is.
private void	CASH.initHeap(ObjectHeap<CASHInterval> heap, Relation<ParameterizationFunction> relation, int dim, DBIDs ids) Initializes the heap with the root intervals.
private java.util.List<ORCLUS.ORCLUSCluster>	ORCLUS.initialSeeds(Relation<V> database, int k) Initializes the list of seeds wit a random sample of size k.
private void	ORCLUS.merge(Relation<V> relation, java.util.List<ORCLUS.ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress) Reduces the number of seeds to k_new
private Relation<ParameterizationFunction>	CASH.preprocess(Relation<V> vrel) Preprocess the dataset, precomputing the parameterization functions.
private ORCLUS.ProjectedEnergy	ORCLUS.projectedEnergy(Relation<V> relation, ORCLUS.ORCLUSCluster c_i, ORCLUS.ORCLUSCluster c_j, int i, int j, int dim) Computes the projected energy of the specified clusters.
Clustering<Model>	LMCLUS.run(Database database, Relation<NumberVector> relation) The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
CorrelationClusterOrder	HiCO.run(Database db, Relation<V> relation)
Clustering<DimensionModel>	COPAC.run(Database database, Relation<V> relation) Run the COPAC algorithm.
Clustering<CorrelationModel>	ERiC.run(Database database, Relation<V> relation) Performs the ERiC algorithm on the given database.
Clustering<Model>	ORCLUS.run(Database database, Relation<V> relation) Performs the ORCLUS algorithm on the given database.
Clustering<Model>	CASH.run(Relation<V> rel) Run CASH on the relation.
private double[][]	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dim, CASHInterval interval, ModifiableDBIDs ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
private LinearEquationSystem	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dimensionality, DBIDs ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
private ORCLUS.ORCLUSCluster	ORCLUS.union(Relation<V> relation, ORCLUS.ORCLUSCluster c1, ORCLUS.ORCLUSCluster c2, int dim) Returns the union of the two specified clusters.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with parameters of type Relation

Constructor and Description
Instance(Database db, Relation<V> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash declared as Relation

Modifier and Type	Field and Description
private Relation<ParameterizationFunction>	CASHIntervalSplit.database The database storing the parameterization functions.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash with parameters of type Relation

Constructor and Description
CASHIntervalSplit(Relation<ParameterizationFunction> database, int minPts) Initializes the logger and sets the debug status to the given value.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.em with parameters of type Relation

Modifier and Type	Method and Description
static double	EM.assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, java.util.List<? extends EMClusterModel<?>> models, WritableDataStore<double[]> probClusterIGivenX) Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
java.util.List<TextbookMultivariateGaussianModel>	TextbookMultivariateGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
java.util.List<DiagonalGaussianModel>	DiagonalGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
java.util.List<SphericalGaussianModel>	SphericalGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
java.util.List<MultivariateGaussianModel>	MultivariateGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
java.util.List<? extends EMClusterModel<M>>	EMClusterModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df) Build the initial models
java.util.List<TwoPassMultivariateGaussianModel>	TwoPassMultivariateGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
static void	EM.recomputeCovarianceMatrices(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, java.util.List<? extends EMClusterModel<?>> models, double prior) Recompute the covariance matrixes.
Clustering<M>	EM.run(Database database, Relation<V> relation) Performs the EM clustering algorithm on the given database.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan declared as Relation

Modifier and Type	Field and Description
private Relation<? extends NumberVector>	ERiCNeighborPredicate.Instance.relation Vector data relation.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan with parameters of type Relation

Modifier and Type	Method and Description
protected abstract M	AbstractRangeQueryNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<O> relation) Method to compute the actual data model.
protected COPACNeighborPredicate.COPACModel	COPACNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList knnneighbors, Relation<V> relation) COPAC model computation
protected PreDeConNeighborPredicate.PreDeConModel	PreDeConNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<V> relation)
protected PreDeConNeighborPredicate.PreDeConModel	FourCNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<V> relation)
ERiCNeighborPredicate.Instance	ERiCNeighborPredicate.instantiate(Database database, Relation<V> relation) Full instantiation interface.
COPACNeighborPredicate.Instance	COPACNeighborPredicate.instantiate(Database database, Relation<V> relation) Full instantiation method.
DataStore<M>	AbstractRangeQueryNeighborPredicate.preprocess(java.lang.Class<? super M> modelcls, Relation<O> relation, RangeQuery<O> query) Perform the preprocessing step.
Clustering<Model>	LSDBC.run(Database database, Relation<O> relation) Run the LSDBC algorithm

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan with parameters of type Relation

Constructor and Description
Instance(DBIDs ids, DataStore<PCAFilteredResult> storage, Relation<? extends NumberVector> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with parameters of type Relation

Modifier and Type	Method and Description
PointerDensityHierarchyRepresentationResult	HDBSCANLinearMemory.run(Database db, Relation<O> relation) Run the algorithm
PointerHierarchyRepresentationResult	NNChain.run(Database db, Relation<O> relation) Run the algorithm
PointerHierarchyRepresentationResult	AGNES.run(Database db, Relation<O> relation) Run the algorithm
PointerPrototypeHierarchyRepresentationResult	MiniMax.run(Database db, Relation<O> relation) Run the algorithm on a database.
PointerHierarchyRepresentationResult	SLINK.run(Database database, Relation<O> relation) Performs the SLINK algorithm on the given database.
PointerHierarchyRepresentationResult	MiniMaxAnderberg.run(Database db, Relation<O> relation) Run the algorithm
PointerPrototypeHierarchyRepresentationResult	MiniMaxNNChain.run(Database db, Relation<O> relation) Run the algorithm
PointerHierarchyRepresentationResult	AnderbergHierarchicalClustering.run(Database db, Relation<O> relation) Run the algorithm
PointerDensityHierarchyRepresentationResult	SLINKHDBSCANLinearMemory.run(Database db, Relation<O> relation) Run the algorithm
private void	SLINK.step2primitive(DBIDRef id, DBIDArrayIter it, int n, Relation<? extends O> relation, PrimitiveDistanceFunction<? super O> distFunc, WritableDoubleDataStore m) Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch with parameters of type Relation

Modifier and Type	Method and Description
CFTree	CFTree.Factory.newTree(DBIDs ids, Relation<? extends NumberVector> relation) Make a new tree.
Clustering<MeanModel>	BIRCHLeafClustering.run(Relation<NumberVector> relation) Run the clustering algorithm.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends NumberVector>	AbstractKMeans.Instance.relation Data relation.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that return Relation

Modifier and Type	Method and Description
Relation<? extends V>	CLARA.CachedDistanceQuery.getRelation()

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type Relation

Modifier and Type	Method and Description
protected Clustering<KMeansModel>	AbstractKMeans.Instance.buildResult(boolean varstat, Relation<? extends NumberVector> relation) Build the result, recomputing the cluster variance if varstat is set to true.
private static double[][]	AbstractKMeans.denseMeans(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation) Returns the mean vectors of the given clusters in the given database.
protected double[][]	AbstractKMeans.initialMeans(Database database, Relation<V> relation) Choose the initial means.
protected static double[][]	AbstractKMeans.means(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation) Returns the mean vectors of the given clusters in the given database.
protected double[][]	KMediansLloyd.Instance.medians(java.util.List<? extends DBIDs> clusters, double[][] medians, Relation<? extends NumberVector> relation) Returns the median vectors of the given clusters in the given database.
Clustering<M>	BestOfMultipleKMeans.run(Database database, Relation<V> relation)
Clustering<M>	KMeans.run(Database database, Relation<V> rel) Run the clustering algorithm.
Clustering<MedoidModel>	FastCLARA.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	SingleAssignmentKMeans.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansSort.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansAnnulus.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansExponion.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansLloyd.run(Database database, Relation<V> relation)
Clustering<MeanModel>	KMediansLloyd.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansElkan.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansHamerly.run(Database database, Relation<V> relation)
Clustering<M>	KMeansBisecting.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansMacQueen.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansMinusMinus.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	KMedoidsPAM.run(Database database, Relation<V> relation) Run k-medoids
Clustering<MedoidModel>	KMedoidsPark.run(Database database, Relation<V> relation) Run k-medoids
Clustering<KMeansModel>	KMeansSimplifiedElkan.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	CLARA.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansCompare.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	CLARANS.run(Database database, Relation<V> relation)
Clustering<M>	XMeans.run(Database database, Relation<V> relation) Run the algorithm on a database and relation.
Clustering<MedoidModel>	FastCLARANS.run(Database database, Relation<V> relation)
private static double[][]	AbstractKMeans.sparseMeans(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends SparseNumberVector> relation) Returns the mean vectors of the given clusters in the given database.
protected double[][]	XMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster, Relation<V> relation) Split an existing centroid into two initial centers.
protected java.util.List<Cluster<M>>	XMeans.splitCluster(Cluster<M> parentCluster, Database database, Relation<V> relation) Conditionally splits the clusters based on the information criterion.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type Relation

Constructor and Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means)
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means)
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization with parameters of type Relation

Modifier and Type	Method and Description
double[][]	PAMInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FirstKInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomUniformGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FarthestPointsInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	KMeansInitialization.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction) Choose initial means
double[][]	ParkInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FarthestSumPointsInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	PredefinedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	LABInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	OstrovskyInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	KMeansPlusPlusInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomNormalGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomlyChosenInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	SampleKMeansInitialization.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
(package private) static void	KMeansPlusPlusInitialMeans.chooseRemaining(Relation<? extends NumberVector> relation, DBIDs ids, DistanceQuery<NumberVector> distQ, int k, java.util.List<NumberVector> means, WritableDoubleDataStore weights, double weightsum, java.util.Random random) Choose remaining means, weighted by distance.
protected static <T> double	OstrovskyInitialMeans.initialWeights(WritableDoubleDataStore weights, Relation<? extends T> relation, DBIDs ids, T first, T second, DistanceQuery<? super T> distQ) Initialize the weight list.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel declared as Relation

Modifier and Type	Field and Description
(package private) Relation<V>	KMeansProcessor.relation Data relation.
private Relation<V>	KMeansProcessor.Instance.relation Data relation.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel with parameters of type Relation

Modifier and Type	Method and Description
Clustering<KMeansModel>	ParallelLloydKMeans.run(Database database, Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel with parameters of type Relation

Constructor and Description
Instance(Relation<V> relation, NumberVectorDistanceFunction<? super V> distance, WritableIntegerDataStore assignment, double[][] means) Constructor.
KMeansProcessor(Relation<V> relation, NumberVectorDistanceFunction<? super V> distance, WritableIntegerDataStore assignment, double[] varsum) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with parameters of type Relation

Modifier and Type	Method and Description
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
static <V extends NumberVector> double	BayesianInformationCriterionZhao.logLikelihoodZhao(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
static int	AbstractKMeansQualityMeasure.numberOfFreeParameters(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering) Compute the number of free parameters.
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.varianceOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Variance contribution of a single cluster.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.meta

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.meta with parameters of type Relation

Modifier and Type	Method and Description
private void	ExternalClustering.attachToRelation(Database database, Relation<?> r, it.unimi.dsi.fastutil.ints.IntArrayList assignment, java.util.ArrayList<java.lang.String> name) Build a clustering from the file result.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional with parameters of type Relation

Modifier and Type	Method and Description
Clustering<ClusterModel>	KNNKernelDensityMinimaClustering.run(Relation<V> relation) Run the clustering algorithm on a data relation.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with parameters of type Relation

Modifier and Type	Method and Description
private Clustering<OPTICSModel>	OPTICSXi.extractClusters(ClusterOrder clusterOrderResult, Relation<?> relation, double ixi, int minpts) Extract clusters from a cluster order result.
Clustering<OPTICSModel>	OPTICSXi.run(Database database, Relation<?> relation)
ClusterOrder	OPTICSList.run(Database db, Relation<O> relation)
abstract ClusterOrder	GeneralizedOPTICS.run(Database db, Relation<O> relation) Run OPTICS on the database.
abstract ClusterOrder	AbstractOPTICS.run(Database db, Relation<O> relation) Run OPTICS on the database.
ClusterOrder	OPTICSHeap.run(Database db, Relation<O> relation)
ClusterOrder	DeLiClu.run(Database database, Relation<V> relation)
ClusterOrder	FastOPTICS.run(Database db, Relation<V> rel) Run the algorithm.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with parameters of type Relation

Constructor and Description
Instance(Database db, Relation<O> relation) Constructor for a single data set.
Instance(Database db, Relation<O> relation) Constructor for a single data set.
Instance(Database db, Relation<O> relation) Constructor for a single data set.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace declared as Relation

Modifier and Type	Field and Description
private Relation<V>	DiSH.Instance.relation Data relation.
private Relation<V>	HiSC.Instance.relation Data relation.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type Relation

Modifier and Type	Method and Description
private java.util.ArrayList<PROCLUS.PROCLUSCluster>	PROCLUS.assignPoints(ArrayDBIDs m_current, long[][] dimensions, Relation<V> database) Assigns the objects to the clusters.
private void	P3C.assignUnassigned(Relation<V> relation, WritableDataStore<double[]> probClusterIGivenX, java.util.List<MultivariateGaussianModel> models, ModifiableDBIDs unassigned) Assign unassigned objects to best candidate based on shortest Mahalanobis distance.
private double	PROCLUS.avgDistance(double[] centroid, DBIDs objectIDs, Relation<V> database, int dimension) Computes the average distance of the objects to the centroid along the specified dimension.
private void	DiSH.buildHierarchy(Relation<V> database, Clustering<SubspaceModel> clustering, java.util.List<Cluster<SubspaceModel>> clusters, int dimensionality) Builds the cluster hierarchy.
private void	DiSH.checkClusters(Relation<V> relation, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap) Removes the clusters with size < minpts from the cluster map and adds them to their parents.
private Clustering<SubspaceModel>	DiSH.computeClusters(Relation<V> database, DiSH.DiSHClusterOrder clusterOrder) Computes the hierarchical clusters according to the cluster order.
private void	P3C.computeFuzzyMembership(Relation<V> relation, java.util.ArrayList<P3C.Signature> clusterCores, ModifiableDBIDs unassigned, WritableDataStore<double[]> probClusterIGivenX, java.util.List<MultivariateGaussianModel> models, int dim) Computes a fuzzy membership with the weights based on which cluster cores each data point is part of.
protected boolean	DOC.dimensionIsRelevant(int dimension, Relation<V> relation, DBIDs points) Utility method to test if a given dimension is relevant as determined via a set of reference points (i.e. if the variance along the attribute is lower than the threshold).
private double	PROCLUS.evaluateClusters(java.util.ArrayList<PROCLUS.PROCLUSCluster> clusters, long[][] dimensions, Relation<V> database) Evaluates the quality of the clusters.
private it.unimi.dsi.fastutil.objects.Object2ObjectOpenCustomHashMap<long[],java.util.List<ArrayModifiableDBIDs>>	DiSH.extractClusters(Relation<V> relation, DiSH.DiSHClusterOrder clusterOrder) Extracts the clusters from the cluster order.
private java.util.List<PROCLUS.PROCLUSCluster>	PROCLUS.finalAssignment(java.util.List<Pair<double[],long[]>> dimensions, Relation<V> database) Refinement step to assign the objects to the final clusters.
private java.util.List<CLIQUESubspace>	CLIQUE.findDenseSubspaceCandidates(Relation<? extends NumberVector> database, java.util.List<CLIQUESubspace> denseSubspaces) Determines the k-dimensional dense subspace candidates from the specified (k-1)-dimensional dense subspaces.
private java.util.List<CLIQUESubspace>	CLIQUE.findDenseSubspaces(Relation<? extends NumberVector> database, java.util.List<CLIQUESubspace> denseSubspaces) Determines the k-dimensional dense subspaces and performs a pruning if this option is chosen.
private long[][]	PROCLUS.findDimensions(ArrayDBIDs medoids, Relation<V> database, DistanceQuery<V> distFunc, RangeQuery<V> rangeQuery) Determines the set of correlated dimensions for each medoid in the specified medoid set.
private java.util.List<Pair<double[],long[]>>	PROCLUS.findDimensions(java.util.ArrayList<PROCLUS.PROCLUSCluster> clusters, Relation<V> database) Refinement step that determines the set of correlated dimensions for each cluster centroid.
protected DBIDs	DOC.findNeighbors(DBIDRef q, long[] nD, ArrayModifiableDBIDs S, Relation<V> relation) Find the neighbors of point q in the given subspace
private java.util.List<CLIQUESubspace>	CLIQUE.findOneDimensionalDenseSubspaceCandidates(Relation<? extends NumberVector> database) Determines the one-dimensional dense subspace candidates by making a pass over the database.
private java.util.List<CLIQUESubspace>	CLIQUE.findOneDimensionalDenseSubspaces(Relation<? extends NumberVector> database) Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.
private void	P3C.findOutliers(Relation<V> relation, java.util.List<MultivariateGaussianModel> models, java.util.ArrayList<P3C.ClusterCandidate> clusterCandidates, ModifiableDBIDs noise) Performs outlier detection by testing the Mahalanobis distance of each point in a cluster against the critical value of the ChiSquared distribution with as many degrees of freedom as the cluster has relevant attributes.
private Pair<long[],ArrayModifiableDBIDs>	DiSH.findParent(Relation<V> relation, Pair<long[],ArrayModifiableDBIDs> child, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap) Returns the parent of the specified cluster
private java.util.Collection<CLIQUEUnit>	CLIQUE.initOneDimensionalUnits(Relation<? extends NumberVector> database) Initializes and returns the one dimensional units.
private boolean	DiSH.isParent(Relation<V> relation, Cluster<SubspaceModel> parent, It<Cluster<SubspaceModel>> iter, int db_dim) Returns true, if the specified parent cluster is a parent of one child of the children clusters.
protected Cluster<SubspaceModel>	DOC.makeCluster(Relation<V> relation, DBIDs C, long[] D) Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.
private SetDBIDs[][]	P3C.partitionData(Relation<V> relation, int bins) Partition the data set into bins bins in each dimension independently.
Clustering<SubspaceModel>	DiSH.run(Database db, Relation<V> relation) Performs the DiSH algorithm on the given database.
Clustering<SubspaceModel>	DOC.run(Database database, Relation<V> relation) Performs the DOC or FastDOC (as configured) algorithm on the given Database.
Clustering<SubspaceModel>	PROCLUS.run(Database database, Relation<V> relation) Performs the PROCLUS algorithm on the given database.
ClusterOrder	HiSC.run(Database db, Relation<V> relation)
Clustering<SubspaceModel>	P3C.run(Database database, Relation<V> relation) Performs the P3C algorithm on the given Database.
Clustering<SubspaceModel>	CLIQUE.run(Relation<? extends NumberVector> relation) Performs the CLIQUE algorithm on the given database.
Clustering<SubspaceModel>	SUBCLU.run(Relation<V> relation) Performs the SUBCLU algorithm on the given database.
private java.util.List<Cluster<Model>>	SUBCLU.runDBSCAN(Relation<V> relation, DBIDs ids, Subspace subspace) Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.
protected Cluster<SubspaceModel>	DOC.runDOC(Database database, Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize) Performs a single run of DOC, finding a single cluster.
protected Cluster<SubspaceModel>	FastDOC.runDOC(Database database, Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize) Performs a single run of FastDOC, finding a single cluster.
private java.util.List<Cluster<SubspaceModel>>	DiSH.sortClusters(Relation<V> relation, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap) Returns a sorted list of the clusters w.r.t. the subspace dimensionality in descending order.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type Relation

Constructor and Description
Instance(Database db, Relation<V> relation) Constructor.
Instance(Database db, Relation<V> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial with parameters of type Relation

Modifier and Type	Method and Description
private java.util.HashMap<java.lang.String,DBIDs>	ByLabelClustering.multipleAssignment(Relation<?> data) Assigns the objects of the database to multiple clusters according to their labels.
Clustering<Model>	ByLabelHierarchicalClustering.run(Relation<?> relation) Run the actual clustering algorithm.
Clustering<Model>	TrivialAllNoise.run(Relation<?> relation)
Clustering<Model>	ByLabelClustering.run(Relation<?> relation) Run the actual clustering algorithm.
Clustering<Model>	TrivialAllInOne.run(Relation<?> relation)
Clustering<Model>	ByModelClustering.run(Relation<Model> relation) Run the actual clustering algorithm.
private java.util.HashMap<java.lang.String,DBIDs>	ByLabelClustering.singleAssignment(Relation<?> data) Assigns the objects of the database to single clusters according to their labels.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain declared as Relation

Modifier and Type	Field and Description
private Relation<? extends UncertainObject>	FDBSCANNeighborPredicate.Instance.relation The relation holding the uncertain objects.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain with parameters of type Relation

Modifier and Type	Method and Description
protected boolean	UKMeans.assignToNearestCluster(Relation<DiscreteUncertainObject> relation, java.util.List<double[]> means, java.util.List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum) Returns a list of clusters.
protected java.util.List<double[]>	UKMeans.means(java.util.List<? extends ModifiableDBIDs> clusters, java.util.List<double[]> means, Relation<DiscreteUncertainObject> database) Returns the mean vectors of the given clusters in the given database.
Clustering<?>	RepresentativeUncertainClustering.run(Database database, Relation<? extends UncertainObject> relation) This run method will do the wrapping.
C	CenterOfMassMetaClustering.run(Database database, Relation<? extends UncertainObject> relation) This run method will do the wrapping.
Clustering<?>	UKMeans.run(Database database, Relation<DiscreteUncertainObject> relation) Run the clustering.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain with parameters of type Relation

Constructor and Description
Instance(double epsilon, int sampleSize, double threshold, Relation<? extends UncertainObject> relation, RandomFactory rand) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.itemsetmining

Methods in de.lmu.ifi.dbs.elki.algorithm.itemsetmining with parameters of type Relation

Modifier and Type	Method and Description
private FPGrowth.FPTree	FPGrowth.buildFPTree(Relation<BitVector> relation, int[] iidx, int items) Build the actual FP-tree structure.
protected java.util.List<OneItemset>	APRIORI.buildFrequentOneItemsets(Relation<? extends SparseFeatureVector<?>> relation, int dim, int needed) Build the 1-itemsets.
protected java.util.List<SparseItemset>	APRIORI.buildFrequentTwoItemsets(java.util.List<OneItemset> oneitems, Relation<BitVector> relation, int dim, int needed, DBIDs ids, ArrayModifiableDBIDs survivors) Build the 2-itemsets.
private DBIDs[]	Eclat.buildIndex(Relation<BitVector> relation, int dim, int minsupp)
private int[]	FPGrowth.countItemSupport(Relation<BitVector> relation, int dim) Count the support of each 1-item.
protected java.util.List<? extends Itemset>	APRIORI.frequentItemsets(java.util.List<? extends Itemset> candidates, Relation<BitVector> relation, int needed, DBIDs ids, ArrayModifiableDBIDs survivors, int length) Returns the frequent BitSets out of the given BitSets with respect to the given database.
protected java.util.List<SparseItemset>	APRIORI.frequentItemsetsSparse(java.util.List<SparseItemset> candidates, Relation<BitVector> relation, int needed, DBIDs ids, ArrayModifiableDBIDs survivors, int length) Returns the frequent BitSets out of the given BitSets with respect to the given database.
FrequentItemsetsResult	Eclat.run(Database db, Relation<BitVector> relation) Run the Eclat algorithm
FrequentItemsetsResult	FPGrowth.run(Database db, Relation<BitVector> relation) Run the FP-Growth algorithm
FrequentItemsetsResult	APRIORI.run(Relation<BitVector> relation) Performs the APRIORI algorithm on the given database.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier with parameters of type Relation

Modifier and Type	Method and Description
private static void	COP.computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids) Recompute the centroid of a set.
private double	GaussianUniformMixture.loglikelihoodNormal(DBIDs objids, SetDBIDs anomalous, CovarianceMatrix builder, Relation<V> relation) Computes the loglikelihood of all normal objects.
OutlierResult	OPTICSOF.run(Database database, Relation<O> relation) Perform OPTICS-based outlier detection.
OutlierResult	DWOF.run(Database database, Relation<O> relation) Performs the Generalized DWOF_SCORE algorithm on the given database by calling all the other methods in the proper order.
OutlierResult	SimpleCOP.run(Database database, Relation<V> data)
OutlierResult	COP.run(Relation<V> relation) Process a single relation.
OutlierResult	GaussianUniformMixture.run(Relation<V> relation) Run the algorithm
OutlierResult	GaussianModel.run(Relation<V> relation) Run the algorithm

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased with parameters of type Relation

Modifier and Type	Method and Description
private void	FastABOD.fastABOD(Database db, Relation<V> relation, DBIDs ids, WritableDoubleDataStore abodvalues, DoubleMinMax minmaxabod) Full kernel-based version.
private boolean	FastABOD.kNNABOD(Database db, Relation<V> relation, DBIDs ids, WritableDoubleDataStore abodvalues, DoubleMinMax minmaxabod) Simpler kNN based, can use more indexing.
OutlierResult	LBABOD.run(Database db, Relation<V> relation) Run LB-ABOD on the data set.
OutlierResult	FastABOD.run(Database db, Relation<V> relation) Run Fast-ABOD on the data set.
OutlierResult	ABOD.run(Database db, Relation<V> relation) Run ABOD on the data set.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with parameters of type Relation

Modifier and Type	Method and Description
private void	CBLOF.computeCBLOFs(Relation<O> relation, NumberVectorDistanceFunction<? super O> distance, WritableDoubleDataStore cblofs, DoubleMinMax cblofMinMax, java.util.List<? extends Cluster<MeanModel>> largeClusters, java.util.List<? extends Cluster<MeanModel>> smallClusters) Compute the CBLOF scores for all the data.
private int	CBLOF.getClusterBoundary(Relation<O> relation, java.util.List<? extends Cluster<MeanModel>> clusters) Compute the boundary index separating the large cluster from the small cluster.
OutlierResult	CBLOF.run(Database database, Relation<O> relation) Runs the CBLOF algorithm on the given database.
OutlierResult	KMeansOutlierDetection.run(Database database, Relation<O> relation) Run the outlier detection algorithm.
OutlierResult	EMOutlier.run(Database database, Relation<V> relation) Runs the algorithm in the timed evaluation part.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.distance

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.distance declared as Relation

Modifier and Type	Field and Description
(package private) Relation<O>	HilOut.HilbertFeatures.relation Relation indexed

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.distance with parameters of type Relation

Modifier and Type	Method and Description
protected DoubleDBIDList	ReferenceBasedOutlierDetection.computeDistanceVector(NumberVector refPoint, Relation<? extends NumberVector> database, PrimitiveDistanceQuery<? super NumberVector> distFunc) Computes for each object the distance to one reference point.
protected abstract DoubleDataStore	AbstractDBOutlier.computeOutlierScores(Database database, Relation<O> relation, double d) computes an outlier score for each object of the database.
protected DoubleDataStore	DBOutlierDetection.computeOutlierScores(Database database, Relation<O> relation, double d)
protected DoubleDataStore	DBOutlierScore.computeOutlierScores(Database database, Relation<O> relation, double d)
OutlierResult	ReferenceBasedOutlierDetection.run(Database database, Relation<? extends NumberVector> relation) Run the algorithm on the given relation.
OutlierResult	HilOut.run(Database database, Relation<O> relation)
OutlierResult	KNNWeightOutlier.run(Database database, Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	ODIN.run(Database database, Relation<O> relation) Run the ODIN algorithm
OutlierResult	AbstractDBOutlier.run(Database database, Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	KNNDD.run(Database database, Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	LocalIsolationCoefficient.run(Database database, Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	KNNOutlier.run(Database database, Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	KNNDD.run(Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	SOS.run(Relation<O> relation) Run the algorithm.
OutlierResult	KNNOutlier.run(Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	KNNSOS.run(Relation<O> relation) Run the algorithm.

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.distance with parameters of type Relation

Constructor and Description
HilbertFeatures(Relation<O> relation, double[] min, double diameter) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	ParallelKNNWeightOutlier.run(Database database, Relation<O> relation) Run the parallel kNN weight outlier detector.
OutlierResult	ParallelKNNOutlier.run(Database database, Relation<O> relation)

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	IntrinsicDimensionalityOutlier.run(Database database, Relation<O> relation) Run the algorithm
OutlierResult	IDOS.run(Database database, Relation<O> relation) Run the algorithm
OutlierResult	ISOS.run(Relation<O> relation) Run the algorithm.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.lof declared as Relation

Modifier and Type	Field and Description
private Relation<? extends NumberVector>	ALOCI.ALOCIQuadTree.relation Relation indexed.

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with parameters of type Relation

Modifier and Type	Method and Description
protected void	INFLO.computeINFLO(Relation<O> relation, ModifiableDBIDs pruned, KNNQuery<O> knnq, WritableDataStore<ModifiableDBIDs> rNNminuskNNs, WritableDoubleDataStore inflos, DoubleMinMax inflominmax) Compute the final INFLO scores.
private void	INFLO.computeNeighborhoods(Relation<O> relation, DataStore<SetDBIDs> knns, ModifiableDBIDs pruned, WritableDataStore<ModifiableDBIDs> rNNminuskNNs) Compute the reverse kNN minus the kNN.
protected void	LoOP.computePDists(Relation<O> relation, KNNQuery<O> knn, WritableDoubleDataStore pdists) Compute the probabilistic distances used by LoOP.
protected double	LoOP.computePLOFs(Relation<O> relation, KNNQuery<O> knn, WritableDoubleDataStore pdists, WritableDoubleDataStore plofs) Compute the LOF values, using the pdist distances.
private int	KDEOS.dimensionality(Relation<O> rel) Ugly hack to allow using this implementation without having a well-defined dimensionality.
protected void	KDEOS.estimateDensities(Relation<O> rel, KNNQuery<O> knnq, DBIDs ids, WritableDataStore<double[]> densities) Perform the kernel density estimation step.
private Pair<Pair<KNNQuery<O>,KNNQuery<O>>,Pair<RKNNQuery<O>,RKNNQuery<O>>>	OnlineLOF.getKNNAndRkNNQueries(Database database, Relation<O> relation, StepProgress stepprog) Get the kNN and rkNN queries for the algorithm.
private Pair<KNNQuery<O>,KNNQuery<O>>	FlexibleLOF.getKNNQueries(Database database, Relation<O> relation, StepProgress stepprog) Get the kNN queries for the algorithm.
protected Pair<KNNQuery<O>,KNNQuery<O>>	LoOP.getKNNQueries(Database database, Relation<O> relation, StepProgress stepprog) Get the kNN queries for the algorithm.
OutlierResult	ALOCI.run(Database database, Relation<O> relation)
OutlierResult	LDF.run(Database database, Relation<O> relation) Run the naive kernel density LOF algorithm.
OutlierResult	SimpleKernelDensityLOF.run(Database database, Relation<O> relation) Run the naive kernel density LOF algorithm.
OutlierResult	INFLO.run(Database database, Relation<O> relation) Run the algorithm
OutlierResult	OnlineLOF.run(Database database, Relation<O> relation) Performs the Generalized LOF_SCORE algorithm on the given database by calling #doRunInTime(Database) and adds a OnlineLOF.LOFKNNListener to the preprocessors.
OutlierResult	SimplifiedLOF.run(Database database, Relation<O> relation) Run the Simple LOF algorithm.
OutlierResult	LOF.run(Database database, Relation<O> relation) Runs the LOF algorithm on the given database.
OutlierResult	KDEOS.run(Database database, Relation<O> rel) Run the KDEOS outlier detection algorithm.
OutlierResult	COF.run(Database database, Relation<O> relation) Runs the COF algorithm on the given database.
OutlierResult	LDOF.run(Database database, Relation<O> relation) Run the algorithm
OutlierResult	FlexibleLOF.run(Database database, Relation<O> relation) Performs the Generalized LOF algorithm on the given database by calling FlexibleLOF.doRunInTime(de.lmu.ifi.dbs.elki.database.ids.DBIDs, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, de.lmu.ifi.dbs.elki.logging.progress.StepProgress).
OutlierResult	VarianceOfVolume.run(Database database, Relation<O> relation) Runs the VOV algorithm on the given database.
OutlierResult	LoOP.run(Database database, Relation<O> relation) Performs the LoOP algorithm on the given database.
OutlierResult	LOCI.run(Database database, Relation<O> relation) Run the algorithm

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with parameters of type Relation

Constructor and Description
ALOCIQuadTree(double[] min, double[] max, double[] shift, int nmin, Relation<? extends NumberVector> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	ParallelSimplifiedLOF.run(Database database, Relation<O> relation)
OutlierResult	ParallelLOF.run(Database database, Relation<O> relation)

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.meta

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.meta with parameters of type Relation

Modifier and Type	Method and Description
private java.util.ArrayList<ArrayDBIDs>	HiCS.buildOneDimIndexes(Relation<? extends NumberVector> relation) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list
private void	HiCS.calculateContrast(Relation<? extends NumberVector> relation, HiCS.HiCSSubspace subspace, java.util.ArrayList<ArrayDBIDs> subspaceIndex, java.util.Random random) Calculates the actual contrast of a given subspace.
private java.util.Set<HiCS.HiCSSubspace>	HiCS.calculateSubspaces(Relation<? extends NumberVector> relation, java.util.ArrayList<ArrayDBIDs> subspaceIndex, java.util.Random random) Identifies high contrast subspaces in a given full-dimensional database.
OutlierResult	ExternalDoubleOutlierScore.run(Database database, Relation<?> relation) Run the algorithm.
OutlierResult	FeatureBagging.run(Database database, Relation<NumberVector> relation) Run the algorithm on a data set.
OutlierResult	HiCS.run(Relation<V> relation) Perform HiCS on a given database.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	CTLuScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	TrimmedMeanApproach.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Run the algorithm.
OutlierResult	TrimmedMeanApproach.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Run the algorithm.
OutlierResult	CTLuMoranScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuMoranScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuMedianAlgorithm.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuMedianAlgorithm.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuZTestOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuZTestOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuMedianMultipleAttributes.run(Database database, Relation<N> spatial, Relation<O> attributes) Run the algorithm
OutlierResult	CTLuMedianMultipleAttributes.run(Database database, Relation<N> spatial, Relation<O> attributes) Run the algorithm
OutlierResult	SOF.run(Database database, Relation<N> spatial, Relation<O> relation) The main run method
OutlierResult	SOF.run(Database database, Relation<N> spatial, Relation<O> relation) The main run method
OutlierResult	CTLuMeanMultipleAttributes.run(Database database, Relation<N> spatial, Relation<O> attributes) Run the algorithm
OutlierResult	CTLuMeanMultipleAttributes.run(Database database, Relation<N> spatial, Relation<O> attributes) Run the algorithm
OutlierResult	SLOM.run(Database database, Relation<N> spatial, Relation<O> relation)
OutlierResult	SLOM.run(Database database, Relation<N> spatial, Relation<O> relation)
OutlierResult	CTLuGLSBackwardSearchAlgorithm.run(Database database, Relation<V> relationx, Relation<? extends NumberVector> relationy) Run the algorithm
OutlierResult	CTLuGLSBackwardSearchAlgorithm.run(Database database, Relation<V> relationx, Relation<? extends NumberVector> relationy) Run the algorithm
OutlierResult	CTLuRandomWalkEC.run(Relation<P> spatial, Relation<? extends NumberVector> relation) Run the algorithm.
OutlierResult	CTLuRandomWalkEC.run(Relation<P> spatial, Relation<? extends NumberVector> relation) Run the algorithm.
private Pair<DBIDVar,java.lang.Double>	CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx, Relation<? extends NumberVector> relationy) Run a single iteration of the GLS-SOD modeling step
private Pair<DBIDVar,java.lang.Double>	CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx, Relation<? extends NumberVector> relationy) Run a single iteration of the GLS-SOD modeling step

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood with parameters of type Relation

Modifier and Type	Method and Description
private DataStore<DBIDs>	ExtendedNeighborhood.Factory.extendNeighborhood(Database database, Relation<? extends O> relation) Method to load the external neighbors.
NeighborSetPredicate	ExternalNeighborhood.Factory.instantiate(Database database, Relation<?> relation)
NeighborSetPredicate	PrecomputedKNearestNeighborNeighborhood.Factory.instantiate(Database database, Relation<? extends O> relation)
NeighborSetPredicate	ExtendedNeighborhood.Factory.instantiate(Database database, Relation<? extends O> relation)
NeighborSetPredicate	NeighborSetPredicate.Factory.instantiate(Database database, Relation<? extends O> relation) Instantiation method.
private DataStore<DBIDs>	ExternalNeighborhood.Factory.loadNeighbors(Database database, Relation<?> relation) Method to load the external neighbors.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted with parameters of type Relation

Modifier and Type	Method and Description
LinearWeightedExtendedNeighborhood	LinearWeightedExtendedNeighborhood.Factory.instantiate(Database database, Relation<? extends O> relation)
UnweightedNeighborhoodAdapter	UnweightedNeighborhoodAdapter.Factory.instantiate(Database database, Relation<? extends O> relation)
WeightedNeighborSetPredicate	WeightedNeighborSetPredicate.Factory.instantiate(Database database, Relation<? extends O> relation) Instantiation method.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace declared as Relation

Modifier and Type	Field and Description
(package private) Relation<? extends NumberVector>	OUTRES.KernelDensityEstimator.relation Relation to retrieve data from

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace with parameters of type Relation

Modifier and Type	Method and Description
protected java.util.ArrayList<java.util.ArrayList<DBIDs>>	AbstractAggarwalYuOutlier.buildRanges(Relation<V> relation) Grid discretization of the data: Each attribute of data is divided into phi equi-depth ranges.
private static double[]	SOD.computePerDimensionVariances(Relation<? extends NumberVector> relation, double[] center, DBIDs neighborhood) Compute the per-dimension variances for the given neighborhood and center.
private DBIDs	SOD.getNearestNeighbors(Relation<V> relation, SimilarityQuery<V> simQ, DBIDRef queryObject) Get the k nearest neighbors in terms of the shared nearest neighbor distance.
OutlierResult	AggarwalYuEvolutionary.run(Database database, Relation<V> relation) Performs the evolutionary algorithm on the given database.
OutlierResult	OUTRES.run(Relation<? extends NumberVector> relation) Main loop for OUTRES
OutlierResult	SOD.run(Relation<V> relation) Performs the SOD algorithm on the given database.
OutlierResult	AggarwalYuNaive.run(Relation<V> relation) Run the algorithm on the given relation.

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace with parameters of type Relation

Constructor and Description
EvolutionarySearch(Relation<V> relation, java.util.ArrayList<java.util.ArrayList<DBIDs>> ranges, int m, java.util.Random random) Constructor.
KernelDensityEstimator(Relation<? extends NumberVector> relation, double eps) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.svm

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.svm with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	LibSVMOneClassOutlierDetection.run(Relation<V> relation) Run one-class SVM.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	ByLabelOutlier.run(Relation<?> relation) Run the algorithm
OutlierResult	TrivialAllOutlier.run(Relation<?> relation) Run the actual algorithm.
OutlierResult	TrivialNoOutlier.run(Relation<?> relation) Run the actual algorithm.
OutlierResult	TrivialAverageCoordinateOutlier.run(Relation<? extends NumberVector> relation) Run the actual algorithm.
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector> vecs, Relation<?> labels) Run the algorithm
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector> vecs, Relation<?> labels) Run the algorithm
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector> vecs, Relation<?> labels) Run the algorithm

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.projection

Methods in de.lmu.ifi.dbs.elki.algorithm.projection that return Relation

Modifier and Type	Method and Description
Relation<DoubleVector>	BarnesHutTSNE.run(Database database, Relation<O> relation)
Relation<DoubleVector>	TSNE.run(Relation<O> relation)
Relation<DoubleVector>	SNE.run(Relation<O> relation)

Methods in de.lmu.ifi.dbs.elki.algorithm.projection with parameters of type Relation

Modifier and Type	Method and Description
<T extends O> AffinityMatrix	GaussianAffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)
<T extends O> AffinityMatrix	AffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale) Compute the affinity matrix.
<T extends O> AffinityMatrix	NearestNeighborAffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)
<T extends O> AffinityMatrix	PerplexityAffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)
<T extends O> AffinityMatrix	IntrinsicNearestNeighborAffinityMatrixBuilder.computeAffinityMatrix(Relation<T> relation, double initialScale)
protected void	AbstractProjectionAlgorithm.removePreviousRelation(Relation<?> relation) Remove the previous relation.
Relation<DoubleVector>	BarnesHutTSNE.run(Database database, Relation<O> relation)
Relation<DoubleVector>	TSNE.run(Relation<O> relation)
Relation<DoubleVector>	SNE.run(Relation<O> relation)

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.statistics

Methods in de.lmu.ifi.dbs.elki.algorithm.statistics with parameters of type Relation

Modifier and Type	Method and Description
private void	EvaluateRetrievalPerformance.computeDistances(ModifiableDoubleDBIDList nlist, DBIDIter query, DistanceQuery<O> distQuery, Relation<O> relation) Compute the distances to the neighbor objects.
protected double	HopkinsStatisticClusteringTendency.computeNNForRealData(KNNQuery<NumberVector> knnQuery, Relation<NumberVector> relation, int dim) Search nearest neighbors for real data members.
void	EvaluateRetrievalPerformance.KNNEvaluator.evaluateKNN(double[] knnperf, ModifiableDoubleDBIDList nlist, Relation<?> lrelation, it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<java.lang.Object> counters, java.lang.Object label) Evaluate by simulating kNN classification for k=1...maxk
private DoubleMinMax	DistanceStatisticsWithClasses.exactMinMax(Relation<O> relation, DistanceQuery<O> distFunc) Compute the exact maximum and minimum.
private void	EvaluateRetrievalPerformance.findMatches(ModifiableDBIDs posn, Relation<?> lrelation, java.lang.Object label) Find all matching objects.
protected void	HopkinsStatisticClusteringTendency.initializeDataExtends(Relation<NumberVector> relation, int dim, double[] min, double[] extend) Initialize the uniform sampling area.
Result	HopkinsStatisticClusteringTendency.run(Database database, Relation<NumberVector> relation) Runs the algorithm in the timed evaluation part.
HistogramResult	RankingQualityHistogram.run(Database database, Relation<O> relation) Process a database
Result	EstimateIntrinsicDimensionality.run(Database database, Relation<O> relation)
CollectionResult<double[]>	DistanceQuantileSampler.run(Database database, Relation<O> rel) Run the distance quantile sampler.
CollectionResult<double[]>	AveragePrecisionAtK.run(Database database, Relation<O> relation, Relation<?> lrelation) Run the algorithm
CollectionResult<double[]>	AveragePrecisionAtK.run(Database database, Relation<O> relation, Relation<?> lrelation) Run the algorithm
EvaluateRetrievalPerformance.RetrievalPerformanceResult	EvaluateRetrievalPerformance.run(Database database, Relation<O> relation, Relation<?> lrelation) Run the algorithm
EvaluateRetrievalPerformance.RetrievalPerformanceResult	EvaluateRetrievalPerformance.run(Database database, Relation<O> relation, Relation<?> lrelation) Run the algorithm
Result	RangeQuerySelectivity.run(Database database, Relation<V> relation)
private ScalesResult	AddUniformScale.run(Relation<? extends NumberVector> rel) Add scales to a single vector relation.
private ScalesResult	AddSingleScale.run(Relation<? extends NumberVector> rel) Add scales to a single vector relation.
private DoubleMinMax	DistanceStatisticsWithClasses.sampleMinMax(Relation<O> relation, DistanceQuery<O> distFunc) Estimate minimum and maximum via sampling.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.timeseries

Methods in de.lmu.ifi.dbs.elki.algorithm.timeseries with parameters of type Relation

Modifier and Type	Method and Description
ChangePoints	OfflineChangePointDetectionAlgorithm.run(Relation<DoubleVector> relation) Executes multiple change point detection for given relation
ChangePoints	OfflineChangePointDetectionAlgorithm.Instance.run(Relation<DoubleVector> relation) Run the change point detection algorithm on a data relation.
ChangePoints	SigniTrendChangeDetection.run(Relation<NumberVector> relation) Executes Signi-Trend for given relation
ChangePoints	SigniTrendChangeDetection.Instance.run(Relation<NumberVector> relation) Process a relation.

Uses of Relation in de.lmu.ifi.dbs.elki.application.greedyensemble

Methods in de.lmu.ifi.dbs.elki.application.greedyensemble that return Relation

Modifier and Type	Method and Description
static Relation<NumberVector>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector> relation, DBIDs skip) Prescale each vector (except when in skip) with the given scaling function.

Methods in de.lmu.ifi.dbs.elki.application.greedyensemble with parameters of type Relation

Modifier and Type	Method and Description
static Relation<NumberVector>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector> relation, DBIDs skip) Prescale each vector (except when in skip) with the given scaling function.

Uses of Relation in de.lmu.ifi.dbs.elki.data

Fields in de.lmu.ifi.dbs.elki.data declared as Relation

Modifier and Type	Field and Description
private Relation<? extends NumberVector>	VectorUtil.SortDBIDsBySingleDimension.data The relation to sort.

Constructors in de.lmu.ifi.dbs.elki.data with parameters of type Relation

Constructor and Description
SortDBIDsBySingleDimension(Relation<? extends NumberVector> data) Constructor.
SortDBIDsBySingleDimension(Relation<? extends NumberVector> data, int dim) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.data.model

Methods in de.lmu.ifi.dbs.elki.data.model with parameters of type Relation

Modifier and Type	Method and Description
static NumberVector	ModelUtil.getPrototype(Model model, Relation<? extends NumberVector> relation) Get the representative vector for a cluster model.
static <V extends NumberVector> V	ModelUtil.getPrototype(Model model, Relation<? extends V> relation, NumberVector.Factory<V> factory) Get (and convert!)
static NumberVector	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends NumberVector> relation, DBIDs ids) Get the representative vector for a cluster model, or compute the centroid.
static <V extends NumberVector> V	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends V> relation, DBIDs ids, NumberVector.Factory<V> factory) Get the representative vector for a cluster model, or compute the centroid.

Constructors in de.lmu.ifi.dbs.elki.data.model with parameters of type Relation

Constructor and Description
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<V> db, double[][] strongEigenvectors, double[][] weakEigenvectors, double[][] similarityMatrix, double[] centroid) Provides a new CorrelationAnalysisSolution holding the specified matrix.
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<V> db, double[][] strongEigenvectors, double[][] weakEigenvectors, double[][] similarityMatrix, double[] centroid, java.text.NumberFormat nf) Provides a new CorrelationAnalysisSolution holding the specified matrix and number format.

Uses of Relation in de.lmu.ifi.dbs.elki.database

Fields in de.lmu.ifi.dbs.elki.database with type parameters of type Relation

Modifier and Type	Field and Description
protected java.util.List<Relation<?>>	AbstractDatabase.relations The relations we manage.

Methods in de.lmu.ifi.dbs.elki.database that return Relation

Modifier and Type	Method and Description
private Relation<?>	HashmapDatabase.addNewRelation(SimpleTypeInformation<?> meta) Add a new representation for the given meta.
protected Relation<?>[]	HashmapDatabase.alignColumns(ObjectBundle pack) Find a mapping from package columns to database columns, eventually adding new database columns when needed.
<O> Relation<O>	Database.getRelation(TypeInformation restriction, java.lang.Object... hints) Get an object representation.
<O> Relation<O>	AbstractDatabase.getRelation(TypeInformation restriction, java.lang.Object... hints)
static Relation<java.lang.String>	DatabaseUtil.guessLabelRepresentation(Database database) Guess a potentially label-like representation, preferring class labels.
static Relation<java.lang.String>	DatabaseUtil.guessObjectLabelRepresentation(Database database) Guess a potentially object label-like representation.

Methods in de.lmu.ifi.dbs.elki.database that return types with arguments of type Relation

Modifier and Type	Method and Description
java.util.Collection<Relation<?>>	Database.getRelations() Get all relations of a database.
java.util.Collection<Relation<?>>	AbstractDatabase.getRelations()

Methods in de.lmu.ifi.dbs.elki.database with parameters of type Relation

Modifier and Type	Method and Description
void	ProxyDatabase.addRelation(Relation<?> relation) Add a new representation.
static java.util.SortedSet<ClassLabel>	DatabaseUtil.getClassLabels(Relation<? extends ClassLabel> database) Retrieves all class labels within the database.
<O> DistanceQuery<O>	Database.getDistanceQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get the distance query for a particular distance function.
<O> DistanceQuery<O>	AbstractDatabase.getDistanceQuery(Relation<O> objQuery, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints)
static <O> KNNQuery<O>	QueryUtil.getKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a KNN query object for the given distance function.
static <O> RangeQuery<O>	QueryUtil.getRangeQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a range query object for the given distance function for radius-based neighbor search.
static <O> RKNNQuery<O>	QueryUtil.getRKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a rKNN query object for the given distance function.
<O> SimilarityQuery<O>	Database.getSimilarityQuery(Relation<O> relation, SimilarityFunction<? super O> similarityFunction, java.lang.Object... hints) Get the similarity query for a particular similarity function.
<O> SimilarityQuery<O>	AbstractDatabase.getSimilarityQuery(Relation<O> objQuery, SimilarityFunction<? super O> similarityFunction, java.lang.Object... hints)
static <O> DistanceQuery<O>	DatabaseUtil.precomputedDistanceQuery(Database database, Relation<O> relation, DistanceFunction<? super O> distf, Logging log) Get (or create) a precomputed distance query for the database.
static <O> KNNQuery<O>	DatabaseUtil.precomputedKNNQuery(Database database, Relation<O> relation, DistanceFunction<? super O> distf, int k) Get (or create) a precomputed kNN query for the database.
static <O> KNNQuery<O>	DatabaseUtil.precomputedKNNQuery(Database database, Relation<O> relation, DistanceQuery<O> dq, int k) Get (or create) a precomputed kNN query for the database.

Constructors in de.lmu.ifi.dbs.elki.database with parameters of type Relation

Constructor and Description
ProxyDatabase(DBIDs ids, Relation<?>... relations) Constructor.

Constructor parameters in de.lmu.ifi.dbs.elki.database with type arguments of type Relation

Constructor and Description
ProxyDatabase(DBIDs ids, java.lang.Iterable<Relation<?>> relations) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.distance

Fields in de.lmu.ifi.dbs.elki.database.query.distance declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends O>	PrimitiveDistanceQuery.relation The data to use for this query
protected Relation<DBID>	DBIDDistanceQuery.relation Relation to query.

Methods in de.lmu.ifi.dbs.elki.database.query.distance that return Relation

Modifier and Type	Method and Description
Relation<? extends O>	PrimitiveDistanceQuery.getRelation()
Relation<? extends O>	DistanceQuery.getRelation() Access the underlying data query.
Relation<? extends DBID>	DBIDDistanceQuery.getRelation()

Constructors in de.lmu.ifi.dbs.elki.database.query.distance with parameters of type Relation

Constructor and Description
DBIDDistanceQuery(Relation<DBID> relation, DBIDDistanceFunction distanceFunction) Constructor.
DBIDRangeDistanceQuery(Relation<DBID> relation, DBIDRangeDistanceFunction distanceFunction) Constructor.
PrimitiveDistanceQuery(Relation<? extends O> relation, PrimitiveDistanceFunction<? super O> distanceFunction) Constructor.
PrimitiveDistanceSimilarityQuery(Relation<? extends O> relation, PrimitiveDistanceFunction<? super O> distanceFunction, PrimitiveSimilarityFunction<? super O> similarityFunction) Constructor.
SpatialPrimitiveDistanceQuery(Relation<? extends V> relation, SpatialPrimitiveDistanceFunction<? super V> distanceFunction)
SpatialPrimitiveDistanceSimilarityQuery(Relation<? extends O> relation, SpatialPrimitiveDistanceFunction<? super O> distanceFunction, PrimitiveSimilarityFunction<? super O> similarityFunction) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.knn

Fields in de.lmu.ifi.dbs.elki.database.query.knn declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends O>	PreprocessorKNNQuery.relation The data to use for this query

Methods in de.lmu.ifi.dbs.elki.database.query.knn that return Relation

Modifier and Type	Method and Description
protected Relation<? extends O>	AbstractDistanceKNNQuery.getRelation() Get the relation to query.

Methods in de.lmu.ifi.dbs.elki.database.query.knn with parameters of type Relation

Modifier and Type	Method and Description
private KNNHeap	LinearScanPrimitiveDistanceKNNQuery.linearScan(Relation<? extends O> relation, DBIDIter iter, O obj, KNNHeap heap) Main loop of the linear scan.
private KNNHeap	LinearScanEuclideanDistanceKNNQuery.linearScan(Relation<? extends O> relation, DBIDIter iter, O obj, KNNHeap heap) Main loop of the linear scan.

Constructors in de.lmu.ifi.dbs.elki.database.query.knn with parameters of type Relation

Constructor and Description
PreprocessorKNNQuery(Relation<? extends O> relation, AbstractMaterializeKNNPreprocessor<O> preprocessor) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.range

Fields in de.lmu.ifi.dbs.elki.database.query.range declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends O>	AbstractSimilarityRangeQuery.relation The data to use for this query

Methods in de.lmu.ifi.dbs.elki.database.query.range that return Relation

Modifier and Type	Method and Description
protected Relation<? extends O>	AbstractDistanceRangeQuery.getRelation() Get the relation to query.

Methods in de.lmu.ifi.dbs.elki.database.query.range with parameters of type Relation

Modifier and Type	Method and Description
private void	LinearScanEuclideanDistanceRangeQuery.linearScan(Relation<? extends O> relation, DBIDIter iter, O obj, double range, ModifiableDoubleDBIDList result) Main loop for linear scan,
private void	LinearScanPrimitiveSimilarityRangeQuery.linearScan(Relation<? extends O> relation, DBIDIter iter, O obj, double range, ModifiableDoubleDBIDList result) Main loop for linear scan,
private void	LinearScanPrimitiveDistanceRangeQuery.linearScan(Relation<? extends O> relation, DBIDIter iter, O obj, double range, ModifiableDoubleDBIDList result) Main loop for linear scan,

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.rknn

Fields in de.lmu.ifi.dbs.elki.database.query.rknn declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends O>	AbstractRKNNQuery.relation The data to use for this query
protected Relation<? extends O>	PreprocessorRKNNQuery.relation The data to use for this query

Constructors in de.lmu.ifi.dbs.elki.database.query.rknn with parameters of type Relation

Constructor and Description
PreprocessorRKNNQuery(Relation<O> database, MaterializeKNNAndRKNNPreprocessor.Factory<O> preprocessor) Constructor.
PreprocessorRKNNQuery(Relation<O> relation, MaterializeKNNAndRKNNPreprocessor<O> preprocessor) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.similarity

Fields in de.lmu.ifi.dbs.elki.database.query.similarity declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends O>	PrimitiveSimilarityQuery.relation The data to use for this query

Methods in de.lmu.ifi.dbs.elki.database.query.similarity that return Relation

Modifier and Type	Method and Description
Relation<? extends O>	PrimitiveSimilarityQuery.getRelation()
Relation<? extends O>	SimilarityQuery.getRelation() Access the underlying data query.

Constructors in de.lmu.ifi.dbs.elki.database.query.similarity with parameters of type Relation

Constructor and Description
PrimitiveSimilarityQuery(Relation<? extends O> relation, PrimitiveSimilarityFunction<? super O> similarityFunction) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.database.relation

Subinterfaces of Relation in de.lmu.ifi.dbs.elki.database.relation

Modifier and Type	Interface and Description
interface	DoubleRelation Interface for double-valued relations.
interface	ModifiableRelation<O> Relations that allow modification.

Classes in de.lmu.ifi.dbs.elki.database.relation that implement Relation

Modifier and Type	Class and Description
class	AbstractRelation<O> Abstract base class for relations.
class	ConvertToStringView Representation adapter that uses toString() to produce a string representation.
class	DBIDView Pseudo-representation that is the object ID itself.
class	MaterializedDoubleRelation Represents a single representation.
class	MaterializedRelation<O> Represents a single representation.
class	ProjectedView<IN,OUT> Projected relation view (non-materialized)
class	ProxyView<O> A virtual partitioning of the database.

Fields in de.lmu.ifi.dbs.elki.database.relation declared as Relation

Modifier and Type	Field and Description
(package private) Relation<? extends O>	RelationUtil.RelationObjectIterator.database The database we use.
(package private) Relation<? extends O>	RelationUtil.CollectionFromRelation.db The database we query.
(package private) Relation<?>	ConvertToStringView.existing The database we use
private Relation<? extends IN>	ProjectedView.inner The wrapped representation where we get the IDs from.
private Relation<O>	ProxyView.inner The wrapped representation where we get the IDs from.

Methods in de.lmu.ifi.dbs.elki.database.relation that return Relation

Modifier and Type	Method and Description
static <V extends NumberVector,T extends NumberVector> Relation<V>	RelationUtil.relationUglyVectorCast(Relation<T> database) An ugly vector type cast unavoidable in some situations due to Generics.

Methods in de.lmu.ifi.dbs.elki.database.relation with parameters of type Relation

Modifier and Type	Method and Description
static <V extends FeatureVector<?>> VectorFieldTypeInformation<V>	RelationUtil.assumeVectorField(Relation<V> relation) Get the vector field type information from a relation.
static double[][]	RelationUtil.computeMinMax(Relation<? extends NumberVector> relation) Determines the minimum and maximum values in each dimension of all objects stored in the given database.
static int	RelationUtil.dimensionality(Relation<? extends SpatialComparable> relation) Get the dimensionality of a database relation.
static <V extends SpatialComparable> java.lang.String	RelationUtil.getColumnLabel(Relation<? extends V> rel, int col) Get the column name or produce a generic label "Column XY".
static <V extends NumberVector> NumberVector.Factory<V>	RelationUtil.getNumberVectorFactory(Relation<V> relation) Get the number vector factory of a database relation.
static double[][]	RelationUtil.relationAsMatrix(Relation<? extends NumberVector> relation, ArrayDBIDs ids) Copy a relation into a double matrix.
static <V extends NumberVector,T extends NumberVector> Relation<V>	RelationUtil.relationUglyVectorCast(Relation<T> database) An ugly vector type cast unavoidable in some situations due to Generics.

Constructors in de.lmu.ifi.dbs.elki.database.relation with parameters of type Relation

Constructor and Description
CollectionFromRelation(Relation<? extends O> db) Constructor.
ConvertToStringView(Relation<?> existing) Constructor.
ProjectedView(Relation<? extends IN> inner, Projection<IN,OUT> projection) Constructor.
ProxyView(DBIDs idview, Relation<O> inner) Constructor.
RelationObjectIterator(DBIDIter iter, Relation<? extends O> database) Full Constructor.
RelationObjectIterator(Relation<? extends O> database) Simplified constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.distance.distancefunction

Fields in de.lmu.ifi.dbs.elki.distance.distancefunction declared as Relation

Modifier and Type	Field and Description
protected Relation<O>	AbstractDatabaseDistanceFunction.Instance.relation Relation to query.
protected Relation<O>	AbstractIndexBasedDistanceFunction.Instance.relation Relation to query.

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction that return Relation

Modifier and Type	Method and Description
Relation<? extends O>	AbstractDatabaseDistanceFunction.Instance.getRelation()
Relation<? extends O>	AbstractIndexBasedDistanceFunction.Instance.getRelation()

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type Relation

Modifier and Type	Method and Description
<O extends DBID> DistanceQuery<O>	AbstractDBIDRangeDistanceFunction.instantiate(Relation<O> database)
<T extends DBID> DistanceQuery<T>	RandomStableDistanceFunction.instantiate(Relation<T> relation)
<T extends O> DistanceQuery<T>	DistanceFunction.instantiate(Relation<T> relation) Instantiate with a database to get the actual distance query.
default <T extends O> DistanceQuery<T>	PrimitiveDistanceFunction.instantiate(Relation<T> relation)
<T extends O> SharedNearestNeighborJaccardDistanceFunction.Instance<T>	SharedNearestNeighborJaccardDistanceFunction.instantiate(Relation<T> database)
default <T extends V> SpatialPrimitiveDistanceQuery<T>	SpatialPrimitiveDistanceFunction.instantiate(Relation<T> relation)

Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type Relation

Constructor and Description
Instance(Relation<O> relation, DistanceFunction<? super O> parent) Constructor.
Instance(Relation<O> relation, I index, F parent) Constructor.
Instance(Relation<T> database, SharedNearestNeighborIndex<T> preprocessor, SharedNearestNeighborJaccardDistanceFunction<T> parent) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with parameters of type Relation

Modifier and Type	Method and Description
abstract <T extends O> DistanceQuery<T>	AbstractSimilarityAdapter.instantiate(Relation<T> database)
<T extends O> DistanceQuery<T>	LnSimilarityAdapter.instantiate(Relation<T> database)
<T extends O> DistanceQuery<T>	ArccosSimilarityAdapter.instantiate(Relation<T> database)
<T extends O> DistanceQuery<T>	LinearAdapterLinear.instantiate(Relation<T> database)

Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with parameters of type Relation

Constructor and Description
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<? super O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<? super O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<O> similarityQuery) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.distance.distancefunction.external

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.external with parameters of type Relation

Modifier and Type	Method and Description
<O extends DBID> DistanceQuery<O>	FileBasedSparseFloatDistanceFunction.instantiate(Relation<O> relation)
<O extends DBID> DistanceQuery<O>	FileBasedSparseDoubleDistanceFunction.instantiate(Relation<O> relation)

Uses of Relation in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic with parameters of type Relation

Modifier and Type	Method and Description
<T extends NumberVector> SpatialPrimitiveDistanceSimilarityQuery<T>	HellingerDistanceFunction.instantiate(Relation<T> database)

Uses of Relation in de.lmu.ifi.dbs.elki.distance.distancefunction.set

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.set with parameters of type Relation

Modifier and Type	Method and Description
<T extends FeatureVector<?>> DistanceSimilarityQuery<T>	JaccardSimilarityDistanceFunction.instantiate(Relation<T> relation)

Uses of Relation in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace with parameters of type Relation

Modifier and Type	Method and Description
<T extends NumberVector> SpatialPrimitiveDistanceQuery<T>	SubspaceLPNormDistanceFunction.instantiate(Relation<T> database)

Uses of Relation in de.lmu.ifi.dbs.elki.distance.similarityfunction

Fields in de.lmu.ifi.dbs.elki.distance.similarityfunction declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends DBID>	AbstractDBIDSimilarityFunction.database The database we work on
protected Relation<O>	AbstractIndexBasedSimilarityFunction.Instance.relation Relation to query.

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction that return Relation

Modifier and Type	Method and Description
Relation<? extends O>	AbstractIndexBasedSimilarityFunction.Instance.getRelation()

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction with parameters of type Relation

Modifier and Type	Method and Description
<T extends NumberVector> SpatialPrimitiveDistanceSimilarityQuery<T>	Kulczynski1SimilarityFunction.instantiate(Relation<T> database)
<T extends O> SimilarityQuery<T>	SimilarityFunction.instantiate(Relation<T> relation) Instantiate with a representation to get the actual similarity query.
default <T extends O> SimilarityQuery<T>	PrimitiveSimilarityFunction.instantiate(Relation<T> relation)
<T extends O> IndexBasedSimilarityFunction.Instance<T,?>	IndexBasedSimilarityFunction.instantiate(Relation<T> database) Preprocess the database to get the actual distance function.
abstract <T extends O> AbstractIndexBasedSimilarityFunction.Instance<T,?>	AbstractIndexBasedSimilarityFunction.instantiate(Relation<T> database)
<T extends O> FractionalSharedNearestNeighborSimilarityFunction.Instance<T>	FractionalSharedNearestNeighborSimilarityFunction.instantiate(Relation<T> database)
<T extends O> SharedNearestNeighborSimilarityFunction.Instance<T>	SharedNearestNeighborSimilarityFunction.instantiate(Relation<T> database)

Constructors in de.lmu.ifi.dbs.elki.distance.similarityfunction with parameters of type Relation

Constructor and Description
AbstractDBIDSimilarityFunction(Relation<? extends DBID> database) Constructor.
Instance(Relation<O> relation, I index) Constructor.
Instance(Relation<O> database, SharedNearestNeighborIndex<O> preprocessor, SharedNearestNeighborSimilarityFunction<? super O> similarityFunction) Constructor.
Instance(Relation<T> database, SharedNearestNeighborIndex<T> preprocessor, FractionalSharedNearestNeighborSimilarityFunction<? super T> similarityFunction) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster with parameters of type Relation

Modifier and Type	Method and Description
<T extends Cluster<?>> DistanceSimilarityQuery<T>	ClusterIntersectionSimilarityFunction.instantiate(Relation<T> relation)
<T extends Cluster<?>> DistanceSimilarityQuery<T>	ClusterJaccardSimilarityFunction.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringRandIndexSimilarityFunction.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringFowlkesMallowsSimilarityFunction.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringDistanceSimilarityFunction.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringBCubedF1SimilarityFunction.instantiate(Relation<T> relation)
<T extends Clustering<?>> DistanceSimilarityQuery<T>	ClusteringAdjustedRandIndexSimilarityFunction.instantiate(Relation<T> relation)

Uses of Relation in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Relation

Modifier and Type	Method and Description
<T extends NumberVector> DistanceSimilarityQuery<T>	PolynomialKernelFunction.instantiate(Relation<T> database)

Constructors in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Relation

Constructor and Description
KernelMatrix(PrimitiveSimilarityFunction<? super O> kernelFunction, Relation<? extends O> relation, DBIDs ids) Provides a new kernel matrix.
KernelMatrix(SimilarityQuery<? super O> kernelFunction, Relation<? extends O> relation, DBIDs ids) Provides a new kernel matrix.

Uses of Relation in de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with parameters of type Relation

Modifier and Type	Method and Description
static int	EvaluateSimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption) Compute centroids.
protected double[]	EvaluateConcordantPairs.computeWithinDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, int withinPairs)
double	EvaluateSquaredErrors.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateConcordantPairs.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateVarianceRatioCriteria.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateSimplifiedSilhouette.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluatePBMIndex.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateDaviesBouldin.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateCIndex.evaluateClustering(Database db, Relation<? extends O> rel, DistanceQuery<O> dq, Clustering<?> c) Evaluate a single clustering.
double	EvaluateDBCV.evaluateClustering(Database db, Relation<O> rel, Clustering<?> cl) Evaluate a single clustering.
double	EvaluateSilhouette.evaluateClustering(Database db, Relation<O> rel, DistanceQuery<O> dq, Clustering<?> c) Evaluate a single clustering.
static int	EvaluateVarianceRatioCriteria.globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption) Update the global centroid.
protected void	EvaluateCIndex.processSingleton(Cluster<?> cluster, Relation<? extends O> rel, DistanceQuery<O> dq, DoubleHeap maxDists, DoubleHeap minDists, int w)
double[]	EvaluateDaviesBouldin.withinGroupDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids)

Uses of Relation in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix

Fields in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix declared as Relation

Modifier and Type	Field and Description
(package private) Relation<?>	ComputeSimilarityMatrixImage.SimilarityMatrix.relation The database

Methods in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix that return Relation

Modifier and Type	Method and Description
Relation<?>	ComputeSimilarityMatrixImage.SimilarityMatrix.getRelation() Get the relation

Methods in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix with parameters of type Relation

Modifier and Type	Method and Description
private ComputeSimilarityMatrixImage.SimilarityMatrix	ComputeSimilarityMatrixImage.computeSimilarityMatrixImage(Relation<O> relation, DBIDIter iter) Compute the actual similarity image.

Constructors in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix with parameters of type Relation

Constructor and Description
SimilarityMatrix(java.awt.image.RenderedImage img, Relation<?> relation, ArrayDBIDs ids) Constructor

Uses of Relation in de.lmu.ifi.dbs.elki.index

Fields in de.lmu.ifi.dbs.elki.index declared as Relation

Modifier and Type	Field and Description
protected Relation<O>	AbstractIndex.relation The representation we are bound to.

Methods in de.lmu.ifi.dbs.elki.index with parameters of type Relation

Modifier and Type	Method and Description
Index	IndexFactory.instantiate(Relation<V> relation) Sets the database in the distance function of this index (if existing).

Constructors in de.lmu.ifi.dbs.elki.index with parameters of type Relation

Constructor and Description
AbstractIndex(Relation<O> relation) Constructor.
AbstractRefiningIndex(Relation<O> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.distancematrix

Fields in de.lmu.ifi.dbs.elki.index.distancematrix declared as Relation

Modifier and Type	Field and Description
protected Relation<O>	PrecomputedDistanceMatrix.relation Data relation.

Methods in de.lmu.ifi.dbs.elki.index.distancematrix that return Relation

Modifier and Type	Method and Description
Relation<? extends O>	PrecomputedSimilarityMatrix.PrecomputedSimilarityQuery.getRelation()
Relation<? extends O>	PrecomputedDistanceMatrix.PrecomputedDistanceQuery.getRelation()

Methods in de.lmu.ifi.dbs.elki.index.distancematrix with parameters of type Relation

Modifier and Type	Method and Description
PrecomputedSimilarityMatrix<O>	PrecomputedSimilarityMatrix.Factory.instantiate(Relation<O> relation)
PrecomputedDistanceMatrix<O>	PrecomputedDistanceMatrix.Factory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.distancematrix with parameters of type Relation

Constructor and Description
PrecomputedDistanceMatrix(Relation<O> relation, DBIDRange range, DistanceFunction<? super O> distanceFunction) Constructor.
PrecomputedSimilarityMatrix(Relation<O> relation, SimilarityFunction<? super O> similarityFunction) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.idistance

Methods in de.lmu.ifi.dbs.elki.index.idistance with parameters of type Relation

Modifier and Type	Method and Description
InMemoryIDistanceIndex<V>	InMemoryIDistanceIndex.Factory.instantiate(Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.index.idistance with parameters of type Relation

Constructor and Description
InMemoryIDistanceIndex(Relation<O> relation, DistanceQuery<O> distance, KMedoidsInitialization<O> initialization, int numref) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.invertedlist

Methods in de.lmu.ifi.dbs.elki.index.invertedlist with parameters of type Relation

Modifier and Type	Method and Description
InMemoryInvertedIndex<V>	InMemoryInvertedIndex.Factory.instantiate(Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.index.invertedlist with parameters of type Relation

Constructor and Description
InMemoryInvertedIndex(Relation<V> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.lsh

Methods in de.lmu.ifi.dbs.elki.index.lsh with parameters of type Relation

Modifier and Type	Method and Description
InMemoryLSHIndex.Instance	InMemoryLSHIndex.instantiate(Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.index.lsh with parameters of type Relation

Constructor and Description
Instance(Relation<V> relation, java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super V>> hashfunctions, int numberOfBuckets) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies

Methods in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies with parameters of type Relation

Modifier and Type	Method and Description
java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>>	AbstractProjectedHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector> relation, int l)
java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>>	CosineHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector> relation, int l)
java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super V>>	LocalitySensitiveHashFunctionFamily.generateHashFunctions(Relation<? extends V> relation, int l) Generate hash functions for the given relation.

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed

Fields in de.lmu.ifi.dbs.elki.index.preprocessed declared as Relation

Modifier and Type	Field and Description
protected Relation<O>	AbstractPreprocessorIndex.relation The representation we are bound to.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed with parameters of type Relation

Constructor and Description
AbstractPreprocessorIndex(Relation<O> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics declared as Relation

Modifier and Type	Field and Description
(package private) Relation<V>	RandomProjectedNeighborsAndDensities.points entire point set

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics with parameters of type Relation

Modifier and Type	Method and Description
void	RandomProjectedNeighborsAndDensities.computeSetsBounds(Relation<V> points, int minSplitSize, DBIDs ptList) Create random projections, project points and put points into sets of size about minSplitSize/2

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.knn declared as Relation

Modifier and Type	Field and Description
protected Relation<O>	NaiveProjectedKNNPreprocessor.relation The representation we are bound to.
protected Relation<O>	SpacefillingKNNPreprocessor.relation The representation we are bound to.

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn with parameters of type Relation

Modifier and Type	Method and Description
private MetricalIndexTree<O,N,E>	MetricalIndexApproximationMaterializeKNNPreprocessor.getMetricalIndex(Relation<? extends O> relation) Do some (limited) type checking, then cast the database into a spatial database.
protected SpatialIndexTree<N,E>	SpatialApproximationMaterializeKNNPreprocessor.getSpatialIndex(Relation<O> relation)
SpatialApproximationMaterializeKNNPreprocessor<NumberVector,N,E>	SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector> relation)
KNNJoinMaterializeKNNPreprocessor<O>	KNNJoinMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
MetricalIndexApproximationMaterializeKNNPreprocessor<O,N,E>	MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
NNDescent<O>	NNDescent.Factory.instantiate(Relation<O> relation)
PartitionApproximationMaterializeKNNPreprocessor<O>	PartitionApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
MaterializeKNNPreprocessor<O>	MaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
RandomSampleKNNPreprocessor<O>	RandomSampleKNNPreprocessor.Factory.instantiate(Relation<O> relation)
MaterializeKNNAndRKNNPreprocessor<O>	MaterializeKNNAndRKNNPreprocessor.Factory.instantiate(Relation<O> relation)
abstract AbstractMaterializeKNNPreprocessor<O>	AbstractMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
CachedDoubleDistanceKNNPreprocessor<O>	CachedDoubleDistanceKNNPreprocessor.Factory.instantiate(Relation<O> relation)
NaiveProjectedKNNPreprocessor<V>	NaiveProjectedKNNPreprocessor.Factory.instantiate(Relation<V> relation)
SpacefillingMaterializeKNNPreprocessor<V>	SpacefillingMaterializeKNNPreprocessor.Factory.instantiate(Relation<V> relation)
SpacefillingKNNPreprocessor<V>	SpacefillingKNNPreprocessor.Factory.instantiate(Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.knn with parameters of type Relation

Constructor and Description
AbstractMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor.
CachedDoubleDistanceKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, java.io.File file) Constructor.
KNNJoinMaterializeKNNPreprocessor(Relation<V> relation, DistanceFunction<? super V> distanceFunction, int k) Constructor.
MaterializeKNNAndRKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor.
MaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor with preprocessing step.
MetricalIndexApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor
NaiveProjectedKNNPreprocessor(Relation<O> relation, double window, int projections, RandomProjectionFamily proj, java.util.Random random) Constructor.
NNDescent(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, RandomFactory rnd, double delta, double rho, boolean noInitialNeighbors, int iterations) Constructor.
PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, int partitions, RandomFactory rnd) Constructor
RandomSampleKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, double share, RandomFactory rnd) Constructor.
SpacefillingKNNPreprocessor(Relation<O> relation, java.util.List<SpatialSorter> curvegen, double window, int variants, int odim, RandomProjectionFamily proj, java.util.Random random) Constructor.
SpacefillingMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, java.util.List<SpatialSorter> curvegen, double window, int variants, java.util.Random random) Constructor.
SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.localpca with parameters of type Relation

Modifier and Type	Method and Description
abstract AbstractFilteredPCAIndex<NV>	AbstractFilteredPCAIndex.Factory.instantiate(Relation<NV> relation)
FilteredLocalPCAIndex<NV>	FilteredLocalPCAIndex.Factory.instantiate(Relation<NV> relation) Instantiate the index for a given database.
KNNQueryFilteredPCAIndex<V>	KNNQueryFilteredPCAIndex.Factory.instantiate(Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.localpca with parameters of type Relation

Constructor and Description
AbstractFilteredPCAIndex(Relation<NV> relation, PCARunner pca, EigenPairFilter filter) Constructor.
KNNQueryFilteredPCAIndex(Relation<NV> relation, PCARunner pca, EigenPairFilter filter, KNNQuery<NV> knnQuery, int k) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed.preference

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.preference with parameters of type Relation

Modifier and Type	Method and Description
private long[]	HiSCPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation, DBIDRef id, DBIDs neighborIDs) Determines the preference vector according to the specified neighbor ids.
private long[]	DiSHPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation, ModifiableDBIDs[] neighborIDs, java.lang.StringBuilder msg) Determines the preference vector according to the specified neighbor ids.
private long[]	DiSHPreferenceVectorIndex.determinePreferenceVectorByApriori(Relation<V> relation, ModifiableDBIDs[] neighborIDs, java.lang.StringBuilder msg) Determines the preference vector with the apriori strategy.
private RangeQuery<V>[]	DiSHPreferenceVectorIndex.initRangeQueries(Relation<V> relation, int dimensionality) Initializes the dimension selecting distancefunctions to determine the preference vectors.
DiSHPreferenceVectorIndex<V>	DiSHPreferenceVectorIndex.Factory.instantiate(Relation<V> relation)
PreferenceVectorIndex<V>	PreferenceVectorIndex.Factory.instantiate(Relation<V> relation) Instantiate the index for a given database.
abstract PreferenceVectorIndex<V>	AbstractPreferenceVectorIndex.Factory.instantiate(Relation<V> relation)
HiSCPreferenceVectorIndex<V>	HiSCPreferenceVectorIndex.Factory.instantiate(Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.preference with parameters of type Relation

Constructor and Description
AbstractPreferenceVectorIndex(Relation<NV> relation) Constructor.
DiSHPreferenceVectorIndex(Relation<V> relation, double[] epsilon, int minpts, DiSHPreferenceVectorIndex.Strategy strategy) Constructor.
HiSCPreferenceVectorIndex(Relation<V> relation, double alpha, int k) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed.snn

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.snn with parameters of type Relation

Modifier and Type	Method and Description
SharedNearestNeighborIndex<O>	SharedNearestNeighborIndex.Factory.instantiate(Relation<O> database) Instantiate the index for a given database.
SharedNearestNeighborPreprocessor<O>	SharedNearestNeighborPreprocessor.Factory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.snn with parameters of type Relation

Constructor and Description
SharedNearestNeighborPreprocessor(Relation<O> relation, int numberOfNeighbors, DistanceFunction<O> distanceFunction) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.projected

Fields in de.lmu.ifi.dbs.elki.index.projected declared as Relation

Modifier and Type	Field and Description
(package private) Relation<? extends O>	ProjectedIndex.relation The relation we predend to index.
(package private) Relation<I>	ProjectedIndex.view The view that we really index.

Methods in de.lmu.ifi.dbs.elki.index.projected with parameters of type Relation

Modifier and Type	Method and Description
ProjectedIndex<O,O>	LatLngAsECEFIndex.Factory.instantiate(Relation<O> relation)
ProjectedIndex<O,O>	LngLatAsECEFIndex.Factory.instantiate(Relation<O> relation)
ProjectedIndex<O,I>	ProjectedIndex.Factory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.projected with parameters of type Relation

Constructor and Description
LatLngAsECEFIndex(Relation<? extends O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine) Constructor.
LatLngAsECEFIndex(Relation<? extends O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine) Constructor.
LngLatAsECEFIndex(Relation<? extends O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine) Constructor.
LngLatAsECEFIndex(Relation<? extends O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine) Constructor.
ProjectedIndex(Relation<? extends O> relation, Projection<O,I> proj, Relation<I> view, Index inner, boolean norefine, double kmulti) Constructor.
ProjectedIndex(Relation<? extends O> relation, Projection<O,I> proj, Relation<I> view, Index inner, boolean norefine, double kmulti) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree with parameters of type Relation

Modifier and Type	Method and Description
CoverTree<O>	CoverTree.Factory.instantiate(Relation<O> relation)
SimplifiedCoverTree<O>	SimplifiedCoverTree.Factory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree with parameters of type Relation

Constructor and Description
AbstractCoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
CoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
SimplifiedCoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees with parameters of type Relation

Constructor and Description
AbstractMkTree(Relation<O> relation, PageFile<N> pagefile, S settings) Constructor.
AbstractMkTreeUnified(Relation<O> relation, PageFile<N> pagefile, S settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MkAppTreeIndex.relation The relation indexed

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp with parameters of type Relation

Modifier and Type	Method and Description
MkAppTreeIndex<O>	MkAppTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp with parameters of type Relation

Constructor and Description
MkAppTree(Relation<O> relation, PageFile<MkAppTreeNode<O>> pageFile, MkAppTreeSettings<O> settings) Constructor.
MkAppTreeIndex(Relation<O> relation, PageFile<MkAppTreeNode<O>> pageFile, MkAppTreeSettings<O> settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MkCoPTreeIndex.relation Relation indexed

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop with parameters of type Relation

Modifier and Type	Method and Description
MkCoPTreeIndex<O>	MkCopTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop with parameters of type Relation

Constructor and Description
MkCoPTree(Relation<O> relation, PageFile<MkCoPTreeNode<O>> pagefile, MkTreeSettings<O,MkCoPTreeNode<O>,MkCoPEntry> settings) Constructor.
MkCoPTreeIndex(Relation<O> relation, PageFile<MkCoPTreeNode<O>> pageFile, MkTreeSettings<O,MkCoPTreeNode<O>,MkCoPEntry> settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MkMaxTreeIndex.relation Relation indexed.

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax with parameters of type Relation

Modifier and Type	Method and Description
MkMaxTreeIndex<O>	MkMaxTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax with parameters of type Relation

Constructor and Description
MkMaxTree(Relation<O> relation, PageFile<MkMaxTreeNode<O>> pagefile, MkTreeSettings<O,MkMaxTreeNode<O>,MkMaxEntry> settings) Constructor.
MkMaxTreeIndex(Relation<O> relation, PageFile<MkMaxTreeNode<O>> pagefile, MkTreeSettings<O,MkMaxTreeNode<O>,MkMaxEntry> settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MkTabTreeIndex.relation The relation indexed.

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab with parameters of type Relation

Modifier and Type	Method and Description
MkTabTreeIndex<O>	MkTabTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab with parameters of type Relation

Constructor and Description
MkTabTree(Relation<O> relation, PageFile<MkTabTreeNode<O>> pagefile, MkTreeSettings<O,MkTabTreeNode<O>,MkTabEntry> settings) Constructor.
MkTabTreeIndex(Relation<O> relation, PageFile<MkTabTreeNode<O>> pagefile, MkTreeSettings<O,MkTabTreeNode<O>,MkTabEntry> settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MTreeIndex.relation The relation indexed.

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree with parameters of type Relation

Modifier and Type	Method and Description
MTreeIndex<O>	MTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree with parameters of type Relation

Constructor and Description
MTreeIndex(Relation<O> relation, PageFile<MTreeNode<O>> pagefile, MTreeSettings<O,MTreeNode<O>,MTreeEntry> settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.kd

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.kd with parameters of type Relation

Modifier and Type	Method and Description
MinimalisticMemoryKDTree<O>	MinimalisticMemoryKDTree.Factory.instantiate(Relation<O> relation)
SmallMemoryKDTree<O>	SmallMemoryKDTree.Factory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.kd with parameters of type Relation

Constructor and Description
CountSortAccesses(Counter objaccess, Relation<? extends NumberVector> data) Constructor.
MinimalisticMemoryKDTree(Relation<O> relation, int leafsize) Constructor.
SmallMemoryKDTree(Relation<O> relation, int leafsize) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu

Fields in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu declared as Relation

Modifier and Type	Field and Description
private Relation<O>	DeLiCluTreeIndex.relation The relation we index.

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu with parameters of type Relation

Modifier and Type	Method and Description
DeLiCluTreeIndex<O>	DeLiCluTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu with parameters of type Relation

Constructor and Description
DeLiCluTreeIndex(Relation<O> relation, PageFile<DeLiCluNode> pagefile, RTreeSettings settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat

Fields in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat declared as Relation

Modifier and Type	Field and Description
private Relation<O>	FlatRStarTreeIndex.relation The relation we index

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat with parameters of type Relation

Modifier and Type	Method and Description
FlatRStarTreeIndex<O>	FlatRStarTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat with parameters of type Relation

Constructor and Description
FlatRStarTreeIndex(Relation<O> relation, PageFile<FlatRStarTreeNode> pagefile, RTreeSettings settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query

Fields in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends O>	RStarTreeKNNQuery.relation Relation we query.
protected Relation<? extends O>	RStarTreeRangeQuery.relation Relation we query.

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query with parameters of type Relation

Constructor and Description
EuclideanRStarTreeKNNQuery(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation) Constructor.
EuclideanRStarTreeRangeQuery(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation) Constructor.
RStarTreeKNNQuery(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation, SpatialPrimitiveDistanceFunction<? super O> distanceFunction) Constructor.
RStarTreeRangeQuery(AbstractRStarTree<?,?,?> tree, Relation<? extends O> relation, SpatialPrimitiveDistanceFunction<? super O> distanceFunction) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn

Fields in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn declared as Relation

Modifier and Type	Field and Description
private Relation<O>	RdKNNTree.relation The relation we query.

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn with parameters of type Relation

Modifier and Type	Method and Description
RdKNNTree<O>	RdKNNTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn with parameters of type Relation

Constructor and Description
RdKNNTree(Relation<O> relation, PageFile<RdKNNNode> pagefile, RdkNNSettings settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar

Fields in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar declared as Relation

Modifier and Type	Field and Description
private Relation<O>	RStarTreeIndex.relation Relation

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar with parameters of type Relation

Modifier and Type	Method and Description
RStarTreeIndex<O>	RStarTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar with parameters of type Relation

Constructor and Description
RStarTreeIndex(Relation<O> relation, PageFile<RStarTreeNode> pagefile, RTreeSettings settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.vafile

Methods in de.lmu.ifi.dbs.elki.index.vafile with parameters of type Relation

Modifier and Type	Method and Description
PartialVAFile<V>	PartialVAFile.Factory.instantiate(Relation<V> relation)
VAFile<V>	VAFile.Factory.instantiate(Relation<V> relation)
void	VAFile.setPartitions(Relation<V> relation) Initialize the data set grid by computing quantiles.

Constructors in de.lmu.ifi.dbs.elki.index.vafile with parameters of type Relation

Constructor and Description
DAFile(Relation<? extends NumberVector> relation, int dimension, int partitions) Constructor.
PartialVAFile(int pageSize, Relation<V> relation, int partitions) Constructor.
VAFile(int pageSize, Relation<V> relation, int partitions) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.math.linearalgebra

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra with parameters of type Relation

Modifier and Type	Method and Description
<F extends NumberVector> F	CovarianceMatrix.getMeanVector(Relation<? extends F> relation) Get the mean as vector.
static ProjectedCentroid	ProjectedCentroid.make(long[] dims, Relation<? extends NumberVector> relation) Static Constructor from a relation.
static ProjectedCentroid	ProjectedCentroid.make(long[] dims, Relation<? extends NumberVector> relation, DBIDs ids) Static Constructor from a relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector> relation) Static Constructor from a full relation.
static Centroid	Centroid.make(Relation<? extends NumberVector> relation, DBIDs ids) Static constructor from an existing relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector> relation, DBIDs ids) Static Constructor from a full relation.

Uses of Relation in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with parameters of type Relation

Modifier and Type	Method and Description
double[][]	WeightedCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation) Weighted Covariance Matrix for a set of IDs.
PCAResult	AutotuningPCA.processIds(DBIDs ids, Relation<? extends NumberVector> database)
double[][]	StandardCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database) Compute Covariance Matrix for a collection of database IDs.
double[][]	CovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database) Compute Covariance Matrix for a collection of database IDs.
PCAResult	PCARunner.processIds(DBIDs ids, Relation<? extends NumberVector> database) Run PCA on a collection of database IDs.
double[][]	RANSACCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation)
PCAResult	AutotuningPCA.processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database)
PCAResult	PCARunner.processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database) Run PCA on a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database) Compute Covariance Matrix for a QueryResult Collection.
double[][]	WeightedCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k) Compute Covariance Matrix for a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k) Compute Covariance Matrix for a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processRelation(Relation<? extends NumberVector> relation) Compute Covariance Matrix for a complete relation.

Uses of Relation in de.lmu.ifi.dbs.elki.math.scales

Methods in de.lmu.ifi.dbs.elki.math.scales with parameters of type Relation

Modifier and Type	Method and Description
static LinearScale[]	Scales.calcScales(Relation<? extends SpatialComparable> rel) Compute a linear scale for each dimension.

Uses of Relation in de.lmu.ifi.dbs.elki.math.spacefillingcurves

Constructors in de.lmu.ifi.dbs.elki.math.spacefillingcurves with parameters of type Relation

Constructor and Description
ZCurveTransformer(Relation<? extends NumberVector> relation, DBIDs ids) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.result

Methods in de.lmu.ifi.dbs.elki.result that return types with arguments of type Relation

Modifier and Type	Method and Description
static java.util.List<Relation<?>>	ResultUtil.getRelations(Result r) Collect all Annotation results from a Result

Methods in de.lmu.ifi.dbs.elki.result with parameters of type Relation

Modifier and Type	Method and Description
private DoubleObjPair<Polygon>	KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls, Relation<? extends NumberVector> coords) Recursively step through the clusters to build the hulls.
static SamplingResult	SamplingResult.getSamplingResult(Relation<?> rel) Get the sampling result attached to a relation
static ScalesResult	ScalesResult.getScalesResult(Relation<? extends SpatialComparable> rel) Get (or create) a scales result for a relation.

Method parameters in de.lmu.ifi.dbs.elki.result with type arguments of type Relation

Modifier and Type	Method and Description
private java.lang.StringBuilder	KMLOutputHandler.makeDescription(java.util.Collection<Relation<?>> relations, DBIDRef id) Make an HTML description.

Constructors in de.lmu.ifi.dbs.elki.result with parameters of type Relation

Constructor and Description
SamplingResult(Relation<?> rel) Constructor.
ScalesResult(Relation<? extends SpatialComparable> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.result.textwriter

Method parameters in de.lmu.ifi.dbs.elki.result.textwriter with type arguments of type Relation

Modifier and Type	Method and Description
private void	TextWriter.printObject(TextWriterStream out, Database db, DBIDRef objID, java.util.List<Relation<?>> ra)
private void	TextWriter.writeClusterResult(Database db, StreamFactory streamOpener, Clustering<Model> clustering, Cluster<Model> clus, java.util.List<Relation<?>> ra, NamingScheme naming)
private void	TextWriter.writeOrderingResult(Database db, StreamFactory streamOpener, OrderingResult or, java.util.List<Relation<?>> ra)

Uses of Relation in de.lmu.ifi.dbs.elki.utilities.referencepoints

Methods in de.lmu.ifi.dbs.elki.utilities.referencepoints with parameters of type Relation

Modifier and Type	Method and Description
java.util.Collection<? extends NumberVector>	ReferencePointsHeuristic.getReferencePoints(Relation<? extends NumberVector> db) Get the reference points for the given database.
java.util.Collection<? extends NumberVector>	GridBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	RandomGeneratedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	StarBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	FullDatabaseReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	AxisBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	RandomSampleReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)

Uses of Relation in de.lmu.ifi.dbs.elki.visualization

Fields in de.lmu.ifi.dbs.elki.visualization declared as Relation

Modifier and Type	Field and Description
private Relation<?>	VisualizationTask.relation The main representation

Methods in de.lmu.ifi.dbs.elki.visualization with type parameters of type Relation

Modifier and Type	Method and Description
<R extends Relation<?>> R	VisualizationTask.getRelation()

Constructors in de.lmu.ifi.dbs.elki.visualization with parameters of type Relation

Constructor and Description
VisualizationTask(VisFactory factory, java.lang.String name, java.lang.Object result, Relation<?> relation) Visualization task.

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.parallel3d

Fields in de.lmu.ifi.dbs.elki.visualization.parallel3d declared as Relation

Modifier and Type	Field and Description
(package private) Relation<? extends O>	OpenGL3DParallelCoordinates.Instance.Shared.rel Relation to visualize

Constructors in de.lmu.ifi.dbs.elki.visualization.parallel3d with parameters of type Relation

Constructor and Description
Instance(Relation<? extends O> rel, ProjectionParallel proj, OpenGL3DParallelCoordinates.Settings<O> settings, StylingPolicy stylepol, StyleLibrary stylelib) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.parallel3d.layout

Methods in de.lmu.ifi.dbs.elki.visualization.parallel3d.layout with parameters of type Relation

Modifier and Type	Method and Description
static double[]	AbstractLayout3DPC.computeSimilarityMatrix(DependenceMeasure sim, Relation<? extends NumberVector> rel) Compute a column-wise dependency matrix for the given relation.
Layout	AbstractLayout3DPC.layout(Relation<? extends NumberVector> rel)
Layout	Layouter3DPC.layout(Relation<? extends V> rel) Run the layouting algorithm.

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.projector

Fields in de.lmu.ifi.dbs.elki.visualization.projector declared as Relation

Modifier and Type	Field and Description
(package private) Relation<V>	ParallelPlotProjector.rel Relation we project.
(package private) Relation<V>	HistogramProjector.rel Relation we project.
(package private) Relation<V>	ScatterPlotProjector.rel Relation we project.

Methods in de.lmu.ifi.dbs.elki.visualization.projector that return Relation

Modifier and Type	Method and Description
Relation<V>	ParallelPlotProjector.getRelation() The relation we project.
Relation<V>	HistogramProjector.getRelation() Get the relation we project.
Relation<V>	ScatterPlotProjector.getRelation() The relation we project.

Methods in de.lmu.ifi.dbs.elki.visualization.projector with parameters of type Relation

Modifier and Type	Method and Description
private int	ScatterPlotFactory.dimensionality(Relation<?> rel)
private int	ParallelPlotFactory.dimensionality(Relation<?> rel)

Constructors in de.lmu.ifi.dbs.elki.visualization.projector with parameters of type Relation

Constructor and Description
HistogramProjector(Relation<V> rel, int maxdim) Constructor.
ParallelPlotProjector(Relation<V> rel) Constructor.
ScatterPlotProjector(Relation<V> rel, int maxdim) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.histogram

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.histogram declared as Relation

Modifier and Type	Field and Description
private Relation<NV>	ColoredHistogramVisualizer.Instance.relation The database we visualize

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel declared as Relation

Modifier and Type	Field and Description
protected Relation<NV>	AbstractParallelVisualization.relation The representation we visualize

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends NumberVector>	AbstractScatterplotVisualization.rel The representation we visualize
protected Relation<PolygonsObject>	PolygonVisualization.Instance.rep The representation we visualize
private Relation<?>	TooltipStringVisualization.Instance.result Number value to visualize
private Relation<? extends java.lang.Number>	TooltipScoreVisualization.Instance.result Number value to visualize

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot with parameters of type Relation

Modifier and Type	Method and Description
private void	TooltipStringVisualization.addTooltips(java.lang.String name, Relation<?> rel, VisualizerContext context, Relation<?> rep, ScatterPlotProjector<?> p)
private void	TooltipStringVisualization.addTooltips(java.lang.String name, Relation<?> rel, VisualizerContext context, Relation<?> rep, ScatterPlotProjector<?> p)
private void	TooltipScoreVisualization.addTooltips(java.lang.String nam, Relation<?> val, VisualizerContext context, ScatterPlotProjector<?> p, Relation<?> rel) Add tooltips.
private void	TooltipScoreVisualization.addTooltips(java.lang.String nam, Relation<?> val, VisualizerContext context, ScatterPlotProjector<?> p, Relation<?> rel) Add tooltips.

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.index

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.index with parameters of type Relation

Modifier and Type	Method and Description
static boolean	TreeSphereVisualization.canVisualize(Relation<?> rel, AbstractMTree<?,?,?,?> tree) Test for a visualizable index in the context's database.

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier declared as Relation

Modifier and Type	Field and Description
protected Relation<double[]>	COPVectorVisualization.Instance.result The outlier result to visualize

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.uncertain

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.uncertain declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends UncertainObject>	UncertainBoundingBoxVisualization.Instance.rel The representation we visualize
protected Relation<? extends UncertainObject>	UncertainSamplesVisualization.Instance.rel The representation we visualize

Uses of Relation in tutorial.clustering

Methods in tutorial.clustering with parameters of type Relation

Modifier and Type	Method and Description
protected WritableDataStore<SameSizeKMeansAlgorithm.Meta>	SameSizeKMeansAlgorithm.initializeMeta(Relation<V> relation, double[][] means) Initialize the metadata storage.
protected double[][]	SameSizeKMeansAlgorithm.refineResult(Relation<V> relation, double[][] means, java.util.List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, ArrayModifiableDBIDs tids) Perform k-means style iterations to improve the clustering result.
Result	NaiveAgglomerativeHierarchicalClustering1.run(Database db, Relation<O> relation) Run the algorithm
PointerHierarchyRepresentationResult	NaiveAgglomerativeHierarchicalClustering4.run(Database db, Relation<O> relation) Run the algorithm
Result	NaiveAgglomerativeHierarchicalClustering2.run(Database db, Relation<O> relation) Run the algorithm
Result	NaiveAgglomerativeHierarchicalClustering3.run(Database db, Relation<O> relation) Run the algorithm
Clustering<MeanModel>	SameSizeKMeansAlgorithm.run(Database database, Relation<V> relation) Run k-means with cluster size constraints.
protected void	SameSizeKMeansAlgorithm.updateDistances(Relation<V> relation, double[][] means, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, NumberVectorDistanceFunction<? super V> df) Compute the distances of each object to all means.

Uses of Relation in tutorial.outlier

Methods in tutorial.outlier with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	ODIN.run(Database database, Relation<O> relation) Run the ODIN algorithm Tutorial note: the signature of this method depends on the types that we requested in the ODIN.getInputTypeRestriction() method.
OutlierResult	DistanceStddevOutlier.run(Database database, Relation<O> relation) Run the outlier detection algorithm